Apparatus and methods for particle analysis and autofluorescence discrimination

ABSTRACT

Described herein are apparatuses and methods for analyzing an optical signal decay. In some embodiments, an apparatus includes: a source of a beam of pulsed optical energy; a sample holder configured to expose a sample to the beam; a detector comprising a number of spectral detection channels configured to convert the optical signals into respective electrical signals; and a signal processing module configured to perform a method. In some embodiments, the method includes: receiving the electrical signals from the detector; mathematically combining individual decay curves in the electrical signals into a supercurve, the supercurve comprising a number of components, each component having a time constant and a relative contribution to the supercurve; and quantifying a relative contribution of each component to the supercurve.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/5128,943, entitled “Particle Analysis andSorting Apparatus and Methods”, filed Dec. 22, 2020; and is acontinuation-in-part of U.S. Nonprovisional patent application Ser. No.17/209,057, entitled “Particle Analysis and Sorting Apparatus andMethods”, filed Mar. 22, 2021, the contents of each of which are hereinincorporated by reference in their entirety.

U.S. Nonprovisional patent application Ser. No. 17/209,057, filed Mar.22, 2021 is a continuation of U.S. Nonprovisional patent applicationSer. No. 16/725,332, entitled “Particle Analysis and Sorting Apparatusand Methods”, filed Dec. 23, 2019 and issued as U.S. Pat. No. 10,995,330on Mar. 23, 2021; which is a continuation of U.S. Nonprovisional patentapplication Ser. No. 15/959,653, entitled “Particle Analysis and SortingApparatus and Methods, filed Apr. 23, 2018 and issued as U.S. Pat. No.10,564,088 on Feb. 18, 2020, the contents of each of which are hereinincorporated by reference in their entirety.

U.S. Nonprovisional patent application Ser. No. 15/959,653, filed Apr.23, 2018 claims the priority benefit of U.S. Provisional PatentApplication Ser. No. 62/593,995, entitled “Particle Analysis and SortingApparatus and Methods, filed Dec. 3, 2017, and is a continuation-in-partof U.S. Nonprovisional patent application Ser. No. 15/599,834, entitled“Particle Analysis and Sorting Apparatus and Methods,” filed May 19,2017 and issued as U.S. Pat. No. 9,952,133 on Apr. 24, 2018, thecontents of each of which are herein incorporated by reference in theirentirety.

U.S. Nonprovisional patent application Ser. No. 15/599,834, filed May19, 2017 is a continuation of U.S. Nonprovisional patent applicationSer. No. 14/879,079, entitled “Particle Analysis and Sorting Apparatusand Methods,” filed Oct. 8, 2015 and issued as U.S. Pat. No. 9,658,148on May 23, 2017; which claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/062,133, filed on Oct. 9, 2014, the contents ofeach of which are herein incorporated by reference in their entirety.

GOVERNMENT SUPPORT CLAUSE

This invention was made in part with government support under grantnumbers 1R43GM123906-01, 1R43GM128546-01, 1R43GM131619-01 REVISED,2R44GM123906-02A1, and 5R44GM123906-03 awarded by the NationalInstitutes of Health. The government has certain rights in theinvention.

INTRODUCTION

This disclosure pertains to the fields of Particle Analysis, ParticleSorting, Multiplexed Assays, Imaging, and Microscopy. In particular,embodiments disclosed herein are capable of increased multiplexing,reduced need for spectral compensation, and reduced interference fromautofluorescence in Flow Cytometry, Cell Sorting, Bead-BasedMulti-Analyte Assays, Imaging, and Microscopy.

BACKGROUND

Cellular analysis and sorting have reached a high level ofsophistication, enabling their widespread use in life science researchand medical diagnostics alike. Yet for all their remarkable success astechnologies, much remains to be done in order to meet significant needsin terms of applications.

One area of continuing unmet need is that of multiplexing. Multiplexing(also referred to as multiparameter, high-parameter, multicolor, orpolychromatic assays) refers to the practice of labeling cells, beads,or other particles with multiple types of biochemical or biophysical“tags” simultaneously and detecting those tags uniquely, so as togenerate a richer set of information with each analysis. The mostcommonly used tags in microscopy and flow cytometry are fluorescentmolecules, or fluorophores. A fluorophore may be a naturally occurringfluorophore; it may be an added reagent; it may be a fluorescent protein[like, e.g., Green Fluorescence Protein (GFP)] expressed by geneticmanipulation; it may be a byproduct of chemical or biochemicalreactions, etc. Fluorophores may be used as they are, relying on theirnative affinity for certain subcellular structures such as, e.g., DNA orRNA; or they may be linked to the highly specific biochemical entitiesknown as antibodies, in a process referred to as conjugation. As aparticular antibody binds to a matching antigen, often on the surface ofa cell, the fluorophore conjugated to that antibody becomes a “tag” forthat cell. The presence or absence of the fluorophore (and therefore ofthe antigen the fluorophore-conjugated antibody is intended tospecifically bind to) can then be established by excitation of the cellsin the sample by optical means and the detection (if present) of thefluorescence emission from the fluorophore. Fluorescence emission into acertain range of the optical spectrum, or band, is sometimes referred toin the art as a “color;” the ability to perform multiplexed analysis istherefore sometimes ranked by the number of simultaneous colorsavailable for detection.

The use of multiple distinct tags (and detection of their associatedcolors) simultaneously allows the characterization of each cell to amuch greater degree of detail than possible with the use of a singletag. In immunology particularly, cells are classified based on theirexpression of surface antigens. The identification of a large number ofdifferent surface antigens on various types of cells has motivated thecreation of a rich taxonomy of cell types. To uniquely identify theexact type of cell under analysis, it is therefore often necessary toperform cell analysis protocols involving simultaneously a large numberof distinct antibody-conjugated tags, each specifically designed toidentify the presence of a particular type of antigen on the cellsurface. Tags have also been devised to label intracellular components,such as certain proteins, and nucleic acids, in both single-stranded anddouble-stranded form.

In flow cytometry of the prior art, methods have been devised tosimultaneously label cells with up to thirty or more differentfluorescent tags and detect their respective colors. Commerciallyavailable instrumentation is generally limited to simultaneous detectionof fifteen colors or less, and most commonly less than about ten colors.One of the main challenges of routinely performing highly multiplexedanalysis (as the practice of simultaneously detecting more than about adozen separate colors is sometimes called) is the technical difficultyof keeping detection of each color (and its associated tag) separatefrom detection of all the other ones. FIG. 1 illustrates one key aspectof the challenge of multiplexed measurement of fluorescence in the priorart. The graph in this FIG. 1 depicts various fluorescence emissioncurves (thin solid lines) of intensity (I) as a function of wavelength(A), all curves having been normalized to their respective peakintensities. In applications of fluorescence detection, it is verycommonly desirable to employ several different colors, or spectralbands, of the electromagnetic spectrum, and to assign each band to adifferent fluorophore. Different fluorophores can be selected, on thebasis of their average emission spectra, so as to obtain relativelydense coverage of a certain range of the electromagnetic spectrum, andthereby maximize the amount of information that can be extracted in thecourse of a single experiment or analysis “run.” However, when strivingto maximize spectral coverage, one of the common undesirableconsequences is spectral overlap. The shaded portions in FIG. 1illustrate the problem caused by spectral overlap between adjacentfluorescence spectra. In this particular illustrative example, fivespectral fluorescence “bands” or colors (the five emission curvespeaking at different wavelengths) span a certain desired range of theelectromagnetic spectrum, such as, e.g., the visible portion of thespectrum from about 400 nm to about 700 nm in wavelength. The shadedportions indicate sections of the spectrum where it is impossible, usingspectral means alone, to decide whether the signal comes from one or theother of the two bands adjacent to the overlapping region; accordingly,the portions of the spectrum corresponding to significant overlap arecommonly discarded, resulting in inefficient use of the spectrum.Additionally, even after discarding such portions, residual overlapremains in the other portions, resulting in contamination of one bandfrom signals from other bands. Attempts at negating the deleteriouseffects of such contamination go under the heading of “compensation.”This spectral overlap problem is variously described in the literatureand the community as the “crosstalk,” the “spillover,” the “compensationproblem,” etc., and it is a major factor in limiting the maximum numberof concurrent spectral bands, or colors, that can be employed in afluorescence detection experiment.

Another potential problem in the spectral analysis of cellular samplesis autofluorescence from endogenous fluorophores, i.e., fluorescencefrom molecular species already present in a cell prior to the additionof any external fluorescent labels. Separating or distinguishingautofluorescence from fluorescence due to exogenous biochemical orbiophysical “tags” presents a challenge in many multiplexed analyses.Endogenous fluorophores may include: cyclic ring compounds (e.g., NADH,FAD), aromatic amino acids (e.g., tryptophan), cellular organelles(e.g., mitochondria, lysozyme), collagen and elastin, etc. For example,NADH (reduced form of nicotinamide adenine dinucleotide) and FAD (flavinadenine dinucleotide), two common metabolic cofactors found in livingcells, absorb over a broad range of wavelengths, particularly in theultraviolet (UV) (from about 200 to 400 nm) and blue-violet (400 to 500nm) regions, and their autofluorescence overlaps with the visibleemission spectra of commonly used fluorescent dyes.

It would be desirable, then, to provide a way to perform highlymultiplexed analyses of particles or cells with a reduced or eliminatedimpact of spectral crosstalk and/or autofluorescence.

In cell analysis of the prior art, methods have also been devised tosimultaneously label cells with up to thirty or more different tags anddetect their respective characteristics. For example, the techniqueknown in the art as mass cytometry employs not fluorescence as a way todistinguish different tags, but mass spectrometry, where the tagsincorporate not fluorophores, but different isotopes of rare earthsidentifiable by their mass spectra. One major drawback of this approachis that the protocol of analysis is destructive to the sample, the cellsand their tags becoming elementally vaporized in the process ofgenerating the mass spectra. This approach is therefore not suited tothe selection and sorting of cells or other particles following theiridentification by analysis.

It would be further desirable, then, to provide a way to performselection and optionally sorting of particles or cells based onnondestructive highly multiplexed analysis with a reduced or eliminatedimpact of spectral crosstalk.

In bead-based multiplexing assays of the prior art, the substrate forthe capture of analytes is the surface of a color-coded microsphere(also referred to as “bead”). The measurement of analytes (e.g.,antigens) by so-called sandwich immunoassays is typically performedwith, e.g., antigen-specific primary antibodies attached to the surfaceof the microsphere; the analytes are captured by the primary antibodies;and the reporting is typically performed using, e.g., secondaryantibodies conjugated to fluorescent reporter molecules. Similar methodsof the prior art are used for measurement of other analytes, includingproteins, enzymes, hormones, drugs, nucleic acids, and other biologicaland synthetic molecules. To provide for simultaneous measurement ofdifferent analytes (multiplexing), each microsphere is internallystained with one or more dyes (colors) in precise amounts spanning arange of discrete levels. Each particular level of dye A (and optionallyin combination with particular levels of dye B, dye C, etc.) is assignedto, e.g., a specific primary capture antibody attached to the surface ofthe microsphere. As color-coded beads are mixed with a sample, they eachcapture a certain analyte; a second step provides the secondary bindingof the reporter molecule. The resulting bead+analytes+reporters complexis then passed through a particle analysis apparatus substantially verysimilar to a flow cytometer, where one light source is used to excitethe dye or dyes in each bead, and another light source is used to excitethe reporter fluorophore. The unique color code (combination of specificstaining levels of dye A and optionally dye B, dye C, etc.) assigned toeach capture entity allows the simultaneous analysis of tens or hundredsof analytes in a sample; the dye-based color coding of each bead is usedto classify the results as the beads pass through. In current commercialofferings, there is a practical limit to the number of color-coded beadtypes that can be used simultaneously in a multiplex assay. Onefluorescence detection spectral band is reserved for the reportermolecules, reducing the spectral range available for coding the beads;accordingly, it has been challenging to fashion more than two or threeseparate fluorescence detection bands out of the remaining availablespectrum. Each band providing about 10 discrete levels of fluorescencefor multiplexing, the total number of possible combinations is about 10for one dye, about 100 for two dyes, and about 1,000 for three dyes.Current commercial offerings cap at 500 the number of practicallyavailable multiplexing combinations, limiting the number of individualanalytes that can be examined in a single measurement run.

It would be further desirable, then, to provide a way to performbead-based multiplexing with a greater number of simultaneouslydistinguishable beads, to enable the performance of multiplexing assayswith a greater number of simultaneously measured analytes.

SUMMARY

One aspect of the present disclosure is directed to an apparatus foranalyzing an optical signal decay. In some embodiments, the apparatusincludes: a source of a beam of pulsed optical energy configured toexpose a sample to the beam; a detector comprising a number of spectraldetection channels sensitive to distinct wavelength sections of theelectromagnetic spectrum, such that the channels are configured todetect optical signals resulting from interactions between the beam andthe sample and convert the optical signals into respective electricalsignals; a first optical path from the source of the beam to the sample;a second optical path from the sample to the detector; and a signalprocessing module configured to execute a lifetime analysis algorithm.

In some embodiments, the lifetime analysis algorithm comprises:receiving the electrical signals from the detector, such that theelectrical signals represent a time-domain sequence of pulse signals;segmenting the sequence into equal pulse signal segments each comprisingsubstantially a same number of sampling points, such that each samplingpoint of each pulse signal segment corresponds to a respective samplingindex, and a length of each pulse signal segment corresponds to anexcitation pulse repetition period; coherently adding a value of asampling point corresponding to the respective sampling index of eachpulse signal segment to form a supercurve, such that each sampling indexof the supercurve corresponds to a value equal to a sum of substantiallyall the sampling point values from the corresponding sampling indicesfrom each pulse signal segment, and the supercurve comprises anintensity of at least one lifetime component over time; determiningwhether the at least one lifetime component of the supercurve comprisesone or both of: a short-lifetime component and a long-lifetimecomponent; and quantifying an intensity contribution of the at least onelifetime component of the supercurve.

In some embodiments, the optical signals comprise a fluorescence signal.

In some embodiments, when present, the short-lifetime componentcomprises autofluorescence and the long-lifetime component comprisesexogenous fluorescence.

In some embodiments, the at least one lifetime component comprises aninstrument response function when the short-lifetime component and thelong-lifetime component are not present.

In some embodiments, the sample comprises a suspension of particles,such that the apparatus further comprises: a flow path for thesuspension of particles; and a flowcell configured as an opticalexcitation chamber for generating the optical signals from interactionsbetween the beam of pulsed optical energy and the particles, such thatthe flowcell is connected with the flow path, the first optical path,and the second optical path.

In some embodiments, the apparatus further comprises: a particle sortingactuator connected with the flow path; an actuator driver connected withthe actuator, such that the driver is configured to receive actuationsignals from the signal processing module; and at least one particlecollection receptacle connected with the flow path.

In some embodiments, the particle sorting actuator is based on at leastone flow diversion in the flow path.

In some embodiments, the particle sorting actuator is based on one of: atransient bubble, a pressurizable chamber, apressurizable/depressurizable chamber pair, or a pressure transducer.

In some embodiments, the signal processing module is further configuredto extract time constants from the supercurve.

In some embodiments, the signal processing module is further configuredto allocate each lifetime component of the supercurve to predeterminedbins, each bin representing a group of relatively similar lifetimecomponents.

Another embodiment of the present disclosure is directed to an apparatusfor analyzing an optical signal decay. In some embodiments, theapparatus comprises a source of a beam of pulsed optical energyconfigured to expose a sample to the beam; a detector comprising anumber of spectral detection channels sensitive to distinct wavelengthsections of the electromagnetic spectrum, such that the channels areconfigured to detect optical signals resulting from interactions betweenthe beam and the sample and convert the optical signals into respectiveelectrical signals; a first optical path from the source of the beam tothe sample; a second optical path from the sample to the detector; and asignal processing module configured to execute a lifetime analysisalgorithm.

In some embodiments, the lifetime analysis algorithm comprises receivingthe electrical signals from the detector, such that the electricalsignals represent a time-domain sequence of pulse signals; segmentingthe sequence into equal pulse signal segments each comprisingsubstantially a same number of sampling points, such that each samplingpoint of each pulse signal segment corresponds to a respective samplingindex, and a length of each pulse signal segment corresponds to anexcitation pulse repetition period; coherently adding a value of asampling point corresponding to the respective sampling index of eachpulse signal segment to form a supercurve, such that each sampling indexof the supercurve corresponds to a value equal to a sum of substantiallyall the sampling point values from the corresponding sampling indicesfrom each pulse signal segment, and the supercurve comprises anintensity of at least one lifetime component over time; adding all theintensity values corresponding to each sampling index of the supercurveto generate a sum; normalizing the sum by dividing the sum by a peakvalue of the supercurve; determining a relative intensity contributionof each lifetime component by comparing the normalized sum to a lookuptable; and determining an absolute intensity contribution of eachlifetime component by multiplying the relative intensity contribution bythe peak value of the supercurve.

In some embodiments, the signal processing module is further configuredto subtract a baseline value from each respective sampling point.

In some embodiments, the signal processing module is further configuredto measure a baseline shift produced by the at least one lifetimecomponent as compared to a baseline when the at least one lifetimecomponent is not present.

In some embodiments, the signal processing module is further configuredto subtract the baseline shift from the received electrical signals.

Another aspect of the present disclosure is direct to an apparatus foranalyzing an optical signal decay. In some embodiments, the apparatuscomprises: a source of a beam of pulsed optical energy configured toexpose a sample to the beam, such that the sample comprises one or morefluorophores having at least one lifetime component; a detectorcomprising a number of spectral detection channels sensitive to distinctwavelength sections of the electromagnetic spectrum, such that thechannels are configured to detect optical signals resulting frominteractions between the beam and the sample and convert the opticalsignals into respective electrical signals; a first optical path fromthe source of the beam to the sample; a second optical path from thesample to the detector; and a signal processing module configured toexecute a lifetime analysis algorithm.

In some embodiments, the lifetime analysis algorithm comprises:receiving the electrical signals from the detector, such that theelectrical signals represent a time-domain sequence of pulse signals;segmenting the sequence into equal pulse signal segments each comprisingsubstantially a same number of sampling points, such that a length ofeach segment corresponds to an excitation pulse repetition period;measuring a peak baseline shift of the electrical signals based on apresence of the at least one lifetime component; and calculating anintensity contribution of the at least one lifetime component of thesample based on the measured peak baseline shift and one of: comparisonto a lookup table and numerical fitting.

In some embodiments, the optical signals comprise a fluorescence signal.

In some embodiments, the at least one lifetime component is longer thanthe excitation pulse repetition period and comprises exogenousfluorescence.

In some embodiments, the signal processing module is further configuredto subtract the baseline shift from the received electrical signals.

In some embodiments, the signal processing module is further configuredto: coherently add a value of a sampling point corresponding to arespective sampling index of each pulse signal segment to form asupercurve, such that each sampling index of the supercurve correspondsto a value equal to a sum of substantially all the sampling point valuesfrom the corresponding sampling indices from each pulse signal segment,and the supercurve comprises an intensity of the at least one lifetimecomponent over time; and quantifying an intensity contribution of the atleast one lifetime component of the supercurve.

In some embodiments, the sample comprises a suspension of particles,such that the apparatus further comprises: a flow path for thesuspension of particles; and a flowcell configured as an opticalexcitation chamber for generating the optical signals from interactionsbetween the beam of pulsed optical energy and the particles; such thatthe flowcell is connected with the flow path, the first optical path,and the second optical path.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a wavelength diagram illustrating the spectral overlap(spillover) in multiplexing approaches of the prior art.

FIG. 2 is a time-domain diagram illustrating a frequency-based approachto measuring single-exponential fluorescence lifetime of the prior art.

FIG. 3 is a time-domain diagram illustrating a time-correlatedsingle-photon counting approach to measuring fluorescence lifetime ofthe prior art.

FIG. 4A is a linear-linear time-domain diagram.

FIG. 4B is a log-linear time-domain diagram illustrating asingle-exponential decay curve resulting from pulsed excitation.

FIG. 4C is a linear-linear time-domain diagram.

FIG. 4D is a log-linear time-domain diagram illustrating adouble-exponential decay curve resulting from pulsed excitation.

FIG. 5 is a wavelength-lifetime diagram coupled with a wavelengthdiagram illustrating a multiplexing approach dense in both spectralbands and lifetime bins in accordance with one embodiment.

FIG. 6 is a wavelength-lifetime diagram coupled with a wavelengthdiagram illustrating a multiplexing approach sparse in both spectralbands and lifetime bins in accordance with one embodiment.

FIG. 7 is a schematic illustration of a system configuration of anapparatus for analysis of single particles in a sample in accordancewith one embodiment.

FIG. 8 is a schematic illustration of a system configuration of anapparatus for analysis and sorting of single particles in a sample inaccordance with one embodiment.

FIG. 9 is a schematic representation of the light collection anddetection subsystem of a particle analyzer/sorter with a single spectraldetection band in accordance with one embodiment.

FIG. 10 is a schematic representation of the light collection anddetection subsystem of a particle analyzer/sorter with multiple spectraldetection bands in accordance with one embodiment.

FIG. 11A is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: interaction envelope due toa flowing particle crossing the beam.

FIG. 11B is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: excitation pulses.

FIG. 11C is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: effective excitation pulses.

FIG. 11D is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: fluorescence emission pulseswith decay curves.

FIG. 11E is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: segmentation of individualpulse signals.

FIG. 11F is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: construction of asupercurve.

FIG. 12A is a log-linear time-domain diagram illustrating atriple-exponential decay supercurve constructed from individual pulsesignals resulting from pulsed excitation.

FIG. 12B is a log-linear time-domain diagram illustrating the process ofcomputing successive index-pair differences, determining supercurve kneepoints, and determining supercurve time-constant branches.

FIG. 13A is a schematic plan-view illustration of one step, or state, ofa particle analysis/sorting method that uses a sorting actuator inaccordance with one embodiment.

FIG. 13B is a schematic plan-view illustration of one step, or state, ofa particle analysis/sorting method that uses a sorting actuator inaccordance with one embodiment.

FIG. 14A is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andone-sided actuation in accordance with one embodiment.

FIG. 14B is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andone-sided actuation in accordance with one embodiment.

FIG. 15A is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andone-sided actuation in accordance with one embodiment.

FIG. 15B is a schematic cross-sectional illustration of one step, orstates, of a particle analysis/sorting method with two sorting statesand one-sided actuation in accordance with one embodiment.

FIG. 16A is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andtwo-sided actuation in accordance with one embodiment.

FIG. 16B is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andtwo-sided actuation in accordance with one embodiment.

FIG. 17A is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 17B is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 17C is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 17D is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 18A is a flow chart describing a sequence of principal operationsinvolved in the performance of a method of lifetime analysis inaccordance with one embodiment.

FIG. 18B is a flow chart describing a sequence of principal operationsinvolved in the performance of a method of particle analysis inaccordance with one embodiment.

FIG. 18C is a flow chart describing a sequence of principal operationsinvolved in the performance of a method of highly multiplexed particleanalysis in accordance with one embodiment.

FIG. 19 is a schematic representation of a data processing system toprovide an analyzer/sorter in accordance with one embodiment.

FIG. 20A is a time-domain diagram illustrating computation of an areaunder the supercurve in accordance with one embodiment.

FIG. 20B is a time-domain diagram illustrating computation of an areaunder the supercurve in accordance with one embodiment.

FIG. 20C is a time-domain diagram illustrating computation of an areaunder the supercurve in accordance with one embodiment.

FIG. 21A illustrates an example of a fluorescence signal from a samplecomprising one or more fluorophores whose longest fluorescence lifetimeis substantially shorter than the interpulse period in accordance withone embodiment.

FIG. 21B illustrates an example of a fluorescence signal from a samplecomprising one or more fluorophores, one of which has a fluorescencelifetime that is comparable to or longer than the interpulse period inaccordance with one embodiment.

FIG. 22A is a flow cytometry histogram illustrating the high backgroundattributable to autofluorescence in approaches of the prior art.

FIG. 22B is a flow cytometry histogram illustrating separation betweenunstained and stained (with an exogenous fluorophore) cellularpopulations by discriminating autofluorescence based on lifetimecomponent analysis in accordance with one embodiment.

DETAILED DESCRIPTION

One possible solution to the spectral overlap problem in highlymultiplexed particle and cell analysis would be to utilize, besidespectral information, another type of information with which to index orencode the tags used to label cell characteristics. By adding anindependent quantity that can be detected and measured, one cansignificantly increase the number of combinations available to label andidentify cell types. There would follow then a reduced need to fit alarge number of independent spectral bands into a limited region of theelectromagnetic spectrum, since the total number of availablecombinations could be allocated based on two independent quantitiesinstead of just one.

One improvement disclosed herein is used to provide fluorescencelifetime as that independent quantity, to be combined with spectrallabeling to generate a highly multiplexed set of independentcombinations with which to uniquely tag different cell characteristicsor cell types with a reduced or eliminated impact of spectral crosstalk.

Another improvement disclosed herein is used to provide the combinationof fluorescence lifetime and spectral fluorescence labeling to aid notonly in the highly multiplexed analysis of cells or other particles, butalso in the selection and optional sorting of cells or other particleswith a reduced or eliminated impact of spectral crosstalk.

Still another improvement disclosed herein is used to provide separationbetween autofluorescence and exogenous fluorescence using fluorescencelifetime as an independent quantity to aid not only in the highlymultiplexed analysis of cells or other particles, but also in theselection and optional sorting of cells or other particles with areduced or eliminated impact of background attributable toautofluorescence and optionally with a reduced or eliminated impact ofspectral crosstalk from exogenous fluorophores.

Yet another improvement disclosed herein is used to provide thecombination of fluorescence lifetime and spectral fluorescence labelingin bead-based multiplexing for antigen, protein, nucleic-acid, and othermolecular assays. By providing for lifetime multiplexing of the dyesused to color code the beads, the present disclosure greatly expands thenumber of possible combinations that can be used to identify individualbead types. By adding the capability of distinguishing beads based onfluorescence lifetime binning, this number can be increased to 10,000,100,000, or more, leading to orders-of-magnitude reductions in the costof running, e.g., highly multiplexed immunoassays, protein assays, ornucleic-acid assays.

Fluorescence lifetime is an aspect of the fluorescence emission processgoverned by quantum-mechanical laws. Fluorescence is the absorption byan atom or molecule of a packet of optical energy (a photon) of acertain wavelength and the subsequent emission by the same atom ormolecule of a packet of optical energy (another photon) at a longerwavelength. The amount of time elapsed between absorption and emissionvaries stochastically, but given an ensemble of isolated identical atomsor molecules, the frequency distribution of such elapsed times of theentire ensemble follows an exponential decay curve. The time constant ofsuch a curve (the 1/e time) is referred to as the lifetime for thatfluorescence transition for that atom or molecule.

Different molecular entities display different fluorescence transitions,characterized by different optimal wavelengths of optical absorption,different peak wavelengths of optical emission, and differentfluorescence lifetimes. Certain molecular entities display fluorescencetransitions with similar spectral characteristics (the profiles ofemission as a function of wavelength schematically illustrated inFIG. 1) but with different fluorescence lifetimes. And other molecularentities display fluorescence transitions with different spectralcharacteristics but with similar fluorescence lifetimes. Accordingly,molecular entities may be selected based on spectral characteristics(spectral emission profile) and fluorescence lifetime as essentiallyindependent quantities.

In order to use fluorescence lifetime as a multiplexing parameter inparticle analysis, one needs to provide the means to measure it. FIG. 2describes one aspect of measurements of fluorescence lifetime as carriedout in one approach in the prior art. The graph in this FIG. 2 depictstwo curves of optical intensity (I) as a function of time (t). In thisapproach, the intensity of optical excitation (thick solid line) ismodulated at a certain frequency, and the resulting fluorescence signal(thin dashed line) is analyzed. The effect of a finite fluorescencelifetime manifests itself primarily in the phase shift between themodulated excitation and the modulated emission curves. The maindrawback of this approach (so-called “phase-sensitive” or“frequency-domain” fluorescence lifetime) is that it can probeefficiently only one fluorescence lifetime component at a time, and ispoorly suited to analysis of samples where more than one lifetimecomponent should be measured simultaneously. Certain improvementsdisclosed herein are used to overcome this limitation.

FIG. 3 illustrates the principle behind another approach to measurementof fluorescence lifetime in the prior art. This approach has beenreferred to in the literature as Time-Correlated Single-Photon Counting(TCSPC), and has been used particularly in fluorescence lifetime imagingapplications (FLIM). The graph depicted in this FIG. 3 shows two curvesof intensity (I) as a function of time (t), both normalized to unit peakintensity, and a histogram with associated bins. The first curve (thicksolid line) represents any one of many identical excitation pulses usedto interrogate a portion of the sample; the second curve (thin dashedline) represents the inferred fluorescence emission response from theportion of the sample under interrogation. This second curve is notmeasured directly, but is instead inferred by a numerical fit to ahistogram. A typical hypothetical histogram is shown as a series ofboxes superimposed upon the second curve. This histogram represents thefrequency distribution of arrival times of single fluorescence emissionphotons following excitation by a pulse. By exciting the same portion ofthe sample many times, a histogram is collected that faithfully reflectsthe underlying fluorescence decay curve. The main drawback of thisapproach is the very principle it is based on: single-photon counting.The method only works if a single photon is, on average, emitted as aresult of excitation. For typical decay curves, it is not uncommon torequire between tens of thousands and millions of repeated excitationsin order to acquire enough statistics in the histogram for acceptableaccuracy of results. Even at high pulse repetition rates, this approachnecessarily results in dwell times (the time spent acquiring data on asingle portion of the sample) on the order of milliseconds to seconds.Accordingly, TCSPC is an approach that has been successfully applied tostationary samples, but which is poorly suited to samples that arerapidly varying, unstable, flowing, or generally needing to be analyzedrapidly. Certain improvements disclosed herein are used to overcome thislimitation.

FIGS. 4A-D illustrate the importance of direct time-domain measurementsof fluorescence lifetime. In each of FIGS. 4A-D, a graph depicts theevolution in time (t) of the intensity (I) of two curves: the opticalexcitation pulse (shown as thick solid lines 410, 430, 450, and 470, inthe four graphs, respectively) and the optical emission curve (shown asthin dashed lines 425, 445, 465, and 485, in the four graphs,respectively), both being normalized to unit peak intensity. In FIGS. 4Aand 4C, each of the two curves in each graph 420 and 460 are plotted ona linear-linear scale; in FIGS. 4B and 4D, each of the two curves ineach graph 440 and 480 are plotted on a log-linear scale (also known asa “semilog” scale). The graphs 420 and 440 in FIGS. 4A and 4B illustratethe same curves, just plotted on different scales; likewise, the graphs460 and 480 in FIGS. 4C and 4D illustrate the same curves, just plottedon different scales. In fluorescence processes, a molecule (which can benaturally occurring, such as certain dyes, fluorescent proteins, ornative cellular components such as NADH or FAD; induced by geneticmanipulation, such as fluorescent proteins expressed by transfection; orchemically manufactured, such as a large portion of exogenousfluorophores in current use) exhibits a propensity for absorption ofoptical energy within a certain range of wavelengths (referred to as itsabsorption spectrum), followed by emission of optical energy into adifferent range of wavelengths (referred to as its emission spectrum).The process of absorption and emission in fluorescence is governed byquantum mechanics and is influenced by several factors. Some of thosefactors are intrinsic to the molecule; other factors are environmentalfactors. Emission of a fluorescent photon occurs stochastically; given alarge enough collection of identical molecules (an ensemble), thecollective emission of the ensemble will appear to decay over time. Fora homogeneous ensemble (depicted in FIGS. 4A and 4B), the cumulativecurve of fluorescence emission can be represented by a single decaylifetime (shown schematically as τ_(a) 421 in this case). In the semilogplot 440 of FIG. 4B, the single-decay nature of the dashed emissioncurve 445 is evidenced by the presence of a single straight slope(indicated by its corresponding lifetime τ_(a) 441, corresponding to thesame τ_(a) as 421 in plot 420). For a heterogeneous ensemble (depictedin FIGS. 4C and 4D) consisting of, e.g., two distinct populations, thecumulative curve of fluorescence emission can be better represented by acompound function comprising two different decay lifetimes (shownschematically as τ_(b) 461 and τ_(c) 462 in this case). In the semilogplot 480 of FIG. 4D, the double-decay nature of the dashed emissioncurve 485 is evidenced by the presence of two straight-sloped branches(indicated by their respective lifetimes τ_(b) 481 and τ_(c) 482,respectively corresponding to τ_(b) 461 and τ_(c) 462 in plot 460)joined at a “knee.” The shape of the decay curve gives informationregarding the environment of the molecules in the ensemble. Assumingthat the ensemble in FIGS. 4C and 4D consisted of a single kind ofmolecular entity, the appearance of two distinct lifetimes might suggestthat molecules in the ensemble are exposed to two differentenvironmental influences, one of them causing a significant alterationto the native fluorescence lifetime of the molecules. Without a directrecording in real time of the actual shape of the emission decay curveof the ensemble of fluorescence molecules, the distinction between thecases depicted in FIGS. 4A-B and FIGS. 4C-D would be lost, and with itthe information regarding the environment of the molecular species. Theanalysis made possible by this direct time-domain approach can bevariously referred to as multi-component or multi-exponentialfluorescence decay analysis.

A practical example of application of the principle of analysis ofmulti-exponential, or multi-component, fluorescence decay is found inthe analysis of cells. A eukaryotic cell consists primarily of amembrane, a cytoplasm, a nucleus, and various subcellular cytoplasmicstructures. The biochemical microenvironment experienced by a moleculewithin a cell is greatly affected by factors such as the localconcentration of electrolytes, local pH, local temperature, etc. When afluorophore enters a cell, its microenvironment may be very different,depending on whether the fluorophore is freely floating in thecytoplasm, binds to a molecule (e.g., RNA) or to an enzyme or othersubcellular structures within the cytoplasm, or crosses the nuclearmembrane to bind to, e.g., DNA in the nucleus. When exposed to opticalexcitation, the sub-ensemble of fluorophores bound to DNA in the nucleusmay exhibit a very different lifetime from, e.g., the sub-ensemble offluorophores freely floating in the cytoplasm. By analyzing the compounddecay curve of the entire ensemble, one must be able to distinguishbetween the two (or more) different contributions to the lifetime, as anaverage single lifetime will blur the desired information and present anincomplete and/or misleading picture of the situation.

If the cell to be analyzed is stationary (as, e.g., adhered to asubstrate, or grown on a substrate, suitable for placement under amicroscope), existing microscopy tools could be used to spatiallyresolve physical locations within the cell, perform, e.g., highlyrepetitious experiments on single pixels (or voxels, as in certainapplications of confocal scanning microscopy) spanning very smallportions of the cell, and repeat these measurements over all pixels (orvoxels, as in certain applications of confocal scanning microscopy)comprising the cell, e.g., in a two-dimensional array (or, in certainapplications of confocal scanning microscopy, in a three-dimensionalarray), by raster scanning over or otherwise methodically interrogatingthe region of interest. There are however, many instances when thatapproach is not desirable, and it would be instead advantageous for thecell to be analyzed to be moving swiftly past the point ofinterrogation. One instance is when it is desirable to complete a set ofmeasurements on a cell or on a group of cells in a very short time, asis generally the case in clinical diagnostic applications, wheretime-to-results is a critical parameter on which may depend patients'health or lives. A related instance, also of great relevance in clinicaldiagnostics and drug discovery and development, but increasingly alsorecognized as important in basic scientific research, is when it isdesirable to complete a certain set of measurements on a very largecollection of cells in a practical amount of time, so as to generatestatistically relevant results not skewed by the impact of individualoutliers. Another instance is when it is desirable to performmeasurements on a cell in an environment that mimics to the greatestdegree possible the environment of the cell in its native physiologicalstate: As an example, for cells naturally suspended in flowing liquids,such as all blood cells, adhesion to a substrate is a very unnaturalstate that grossly interferes with their native configuration. There areyet instances where the details of the physical location within the cell(details afforded, at a price of both time and money, by high-resolutionmicroscopy, including confocal scanning microscopy) are simply notimportant, but where a proxy for specific locations within the cellwould suffice, given prior knowledge (based on prior offline studies orresults from the literature) about the correlation between specific celllocations and values of the proxy measurement.

There are also instances where, regardless of the speed at which ameasurement is carried out, and therefore regardless of whether themeasurement is performed on a flow cytometer, under a microscope, orunder some other yet experimental conditions, it would be desirable toreduce or eliminate the spectral crossover problem. Many analyticalprotocols in cell biology research, drug discovery, immunology research,and clinical diagnostics are predicated on the concurrent use ofmultiple fluorophores, in order to elucidate various properties ofhighly heterogeneous samples consisting of diverse cell populations,sometimes with uncertain origin or lineage. The spectral crossoverinherent in such concurrent use of available fluorophores presentlylimits the multiplexing abilities of tools in current use—be they cellsorters, cell analyzers, image-based confocal scanning microscopes, orother platform. Various schemes have been developed to quantify andmitigate the deleterious impact of spectral crossover, and are generallyreferred to as compensation correction schemes. These schemes, however,suffer from overcomplexity, time burden, reagent cost burden, lack ofreproducibility, and difficulty in the proper training of operators. Itwould be therefore advantageous to provide various analytical platformswith a way to multiplex complex measurements without the same attendantspectral crossover issue as is currently experienced.

There are yet instances of cellular analysis where it is desirable toperform fluorescence lifetime measurements on molecular species nativeto the systems under study. In this case the process of fluorescence issometime referred to as autofluorescence or endogenous fluorescence, andit does not depend on the introduction of external fluorophores, butrather relies on the intrinsic fluorescence of molecules already present(generally naturally so) in the cell to be analyzed. Endogenousfluorescence is similar to the fluorescence of externally introducedfluorophores, in being subject to similar effects, such as the influenceof the molecular microenvironment on fluorescence lifetime. Accordingly,it would be advantageous to be able to resolve different states ofendogenous fluorescence on an analytical platform, so as to provide forsimple and direct differentiation between cells belonging to differentpopulations known to correlate with different values of endogenousfluorescence lifetime of one or more natively present compounds. Oneexample of practical application of the principle of endogenousfluorescence is in the differential identification of cancer cells fromnormal cells based on metabolic information.

Several approaches of separating autofluorescence from exogenousfluorescence have been used in the prior art including, for example,avoiding specific fluorophores around autofluorescence-associatedwavelengths, filter selection, gating-based strategies, and chemicalquenching. However, these methods and many others require carefulcontrols and complex experimental protocols, algorithms, and analysisschemes. In some instances, cellular autofluorescence has beeneliminated using the extremely long lifetimes of europium chelates inmicroscopy or lanthanide chelates in flow cytometry. However, thesemethods are slow and poorly suited for anything but static orquasi-static imaging. One of the major advantages of flow cytometry isits extremely high throughput (often over 10,000 events/sec). Under flowconditions, each cell typically spends only a few microseconds in themeasuring window. Adapting flow cytometry to measure fluorescencelifetime would require high laser modulation frequencies and highrepetition rates to generate sufficient signal in such a short dwelltime.

The present disclosure advantageously provides a solution fordiscriminating between autofluorescence and exogenous fluorescence.Separation of autofluorescence from exogenous fluorescence is anotherpractical example of an application of multi-exponential, ormulti-component, fluorescence decay analysis. Autofluorescent moleculesin a cell versus biochemical tags attached to or introduced into a cell,when exposed to optical excitation, may each exhibit a very differentlifetime but may overlap in emission spectra. By analyzing the compounddecay curve of the entire ensemble, one must be able to distinguishbetween the two or more different contributions to the lifetime in anyone spectral detection band. Returning to FIGS. 4C-4D, the twostraight-sloped branches may represent, respectively, a short orautofluorescent lifetime component (lifetimes τ_(b) 481 corresponding toτ_(b) 461 in plot 460) and a long or exogenous fluorescence lifetimecomponent (lifetime τ_(c) 482 corresponding to τ_(c) 462 in plot 460),joined at a transition or “knee”.

It will be appreciated by those skilled in the art that measurements ofheterogeneous samples, whether biological or nonbiological, can compriseinterrogation of different types of analytes (e.g., cells, particles,liquid mixtures, etc.). Any given sample, for example, may comprisecells with significant autofluorescence; cells without appreciableautofluorescence; and cells (whether or not autofluorescent) labeled ornot labeled with different exogenous fluorescent tags. Accordingly, thedecays observed in the course of sample interrogation may comprisemultiple fluorescence lifetime components, a single fluorescencelifetime component, or may not comprise any fluorescence lifetimecomponent at all. In this last case, the observed optical signal may besubstantially null (or it may just comprise noise), but alternatively,it may display a pulse, residual from the excitation process due, e.g.,to incomplete spectral filtering of the excitation light wavelength.This pulse may be referred to as the instrument response function (IRF)for a particular detection channel, since it is the pulse that resultson that channel when no fluorescent species (whether autofluorescent orexogenously fluorescent) are present; generally such pulse will have awidth that is limited by the response of the instrument, due tobandwidth limitations in the excitation drive electronics, in theexcitation laser(s), in the detector(s), in the detector amplifier(s),in the digitizer(s), and/or in other electronic circuitry involved inthe transduction of the optical signal, as well as to the shape of theoptical excitation pulse itself. Typically the IRF pulse width isshorter than pulse widths resulting from fluorescent emissions. If suchan IRF pulse is present, it can effectively behave as if it decaysaccording to a characteristic lifetime, even though it comprises nofluorescence. Analysis of the signal can isolate this instance, forexample by treating the IRF as a kind of “fluorescence” with its ownlifetime value, which can be measured by observing unstained samplesknown to be non-autofluorescent. On channels where rejection of thisresidual by optical means is impractical, it is therefore possible toisolate the residual by treating it as a “fluorescent” species of itsown. This allows the use of the same signal processing circuitry andalgorithms used for fluorescence species identification, simply extendedto include the possibility of a residual IRF pulse. Likewise, in asignal that does comprise actual fluorescence decay(s), the IRF canstill be present, convolved with the fluorescence emission. By treatingthe IRF as an effectively (short-) lifetime fluorescent species, thepresent disclosure can isolate it from the desired fluorescent signals(whether from autofluorescence or exogenous fluorescence). As disclosedherein, a “lifetime component” may reflect a truly fluorescent species(whether autofluorescent or exogenously fluorescent) or, on certaindetection channels, it may reflect the IRF residual.

FIG. 5 schematically illustrates a principle of the present disclosure,namely the ability to improve the multiplexing capacity of an analyticalinstrument by using fluorescence lifetime information. At the bottom ofFIG. 5 is a graph 550 depicting various fluorescence emission curves561-565 (thin solid lines) of intensity (I) as a function of wavelength(λ), all curves having been normalized to their respective unit peakintensity. In contrast to the prior art, where distinction betweendifferent fluorophores as endogenous fluorophores, cell labels, “tags”,or “markers” is performed exclusively by spectral means (the horizontalwavelength axis 2 in graph 550), in the present disclosure a separate,orthogonal dimension of analysis is added: the vertical lifetime r axisin the graph 500 at top. Each fluorescence emission curve is representedby a “band”, shown in the figure as a shaded vertical strip 511-515: FL₁(511), FL₂ (512), FL₃ (513), FL₄ (514), FL₅ (515), . . . . Similarly,different fluorescence lifetime values are represented by differentvalues of τ, grouped together in “bins”, shown in the figure as shadedhorizontal strips 521-525: τ₁ (521), τ₂ (522), τ₃ (523), τ₄ (524), τ₅(525), . . . . Each of the bins is intended to schematically represent arelatively similar group of lifetimes: the variation among the variouslifetime values in the τ₁ bin will generally be smaller than thedifference between the average lifetimes of the τ₁ and τ₂ bins, thevariation among the various lifetime values in the τ₂ bin will generallybe smaller than the difference between the average lifetimes of the τ₁and τ₂ bins and will also generally be smaller than the differencebetween the average lifetimes of the τ₂ and τ₃ bins, and so on.

FIG. 5 makes it plain that the wavelength axis and its associated bandsnow represent only one dimension of a virtual plane. The second, addeddimension of this virtual plane is represented by the fluorescencelifetime axis τ and its associated bins in graph 500. The schematicintersections of the wavelength bands and the lifetime bins are shown ingraph 500 as darker shaded regions 530 (of which only a few are labeled)in the λ-τ plane. The increased multiplexing of the present disclosureis exemplified by the fact that, for every one of the spectral(wavelength) bands generally available to current analytical platforms,the present disclosure offers several possible multiplexed lifetimes: Asan example, the fluorescence band FL₂ (512) supports multiplefluorescence lifetime bins τ₁ (521), τ₂ (522), τ₃ (523), τ₄ (524), τ₅(525), . . . . For a system with n distinct fluorescence bands and mdistinct lifetime bins, the total theoretical number of independentcombinations is n×m; to use a practical example, for a system with 6distinct fluorescence bands and 4 distinct lifetime bins, there are6×4=24 mutually independent multiplexed combinations available.

FIG. 5 also describes how a specific example of a multiplexedcombination would be resolved in the present disclosure. From thewavelength axis a particular spectral band (say, FL₃ band 513) isselected for analysis. This particular spectral band in practice wouldbe selected by spectral optical means, such as one or more of thin-filmfilters, dichroic beam splitters, colored glass filters, diffractiongratings, and holographic gratings, or any other spectrally dispersingmeans suitable for the task and designed to pass this band ofwavelengths preferentially over all others. The resulting optical signalcould still comprise any of a number of fluorescence lifetimes,depending on the instrument design and on the nature of the sample. Thespectral optical signal filtered through as FL₃ is detected, convertedto electronic form, and sent to an electronic signal processing unit fordigitization and further elaboration; see FIG. 10. The signal processingunit (further described below in reference to FIGS. 7 and 19) performsan analysis of the decay characteristics of the optical signalscorresponding to the particle under study, and allocates the variouscontributions to the overall signal from each possible band of lifetimevalues. A virtual electronic “bin” corresponding to the specific bin τ₄(524) of lifetimes would receive a value corresponding to the fractionof the signal that could be ascribed as resulting from a lifetime decaywithin the acceptable range relevant for the τ₄ band. The combination ofthe spectral filtering for FL₃ performed optically on the emitted signaland the lifetime filtering for τ₄ performed digitally on theelectronically converted optical signal results in narrowing downanalysis to a single multiplexing element: the shaded intersection 540marked with a thick solid square in graph 500 of FIG. 5.

The specific choice of FL₃ and τ₄ is only illustrative, in the sensethat any of the intersections between detectable spectral fluorescencebands and resolvable lifetime bins are potentially simultaneouslyaccessible by analysis—resulting in the increased multiplexing abilitydescribed above as desirable. FIG. 5 shows explicitly a set of allowablemultiplexed intersections for a prophetic example comprising 5 distinctfluorescence bands and 5 distinct lifetime bins: a resulting set of upto 5×5=25 separate, mutually independent combinations. This example isillustrative only: the number of possible combinations is not limited to25, being given instead by the number of individually separablefluorescence spectral bands multiplied by the number of individuallyseparable lifetime values bins. The theoretical maximum number ofindividually separable lifetime bins is related to the samplingfrequency of digitization, the repetition rate of excitation pulses, andthe duration (width) of each excitation pulse. In the limit ofexcitation pulses much shorter than lifetimes of interest (which aretypically in the tens of picoseconds to tens of nanoseconds, and whichin some cases reach microsecond levels or greater), the maximum numberof separable lifetime bins is given by the pulse repetition perioddivided by twice the digitization sampling period, plus one. Electronic,optical, and other noise effects in actual systems may significantlyreduce this theoretical maximum.

In the case where one of the lifetimes used is comparable to orsubstantially longer than the interpulse period (or an excitation pulserepetition period), which typically may be in the range of 5 to 500 ns,more preferably may be in the range of 10 to 200 ns, and most preferablymay be in the range of 20 to 100 ns, the decay may be so comparativelyslow as to produce an effectively flat optical emission baseline shift.One of skill in the art will appreciate that the terms interpulse periodand excitation pulse repetition period may be used interchangeablyherein. For example, fluorescence from common fluorophores typically hasa longer lifetime (from about 4 ns or less to about 7 ns or more), whileautofluorescence from cells has a predominantly short lifetime range,for example ranging from about 0.5 ns or less to about 3 ns or more.Fluorescence from certain nanocrystals (also referred to as inorganicnanoparticles or quantum dots) is longer still, ranging from about 15 orless to about 100 ns or more. For example, certain lanthanide complexes,including, without limitation, complexes of praseodymium, neodymium,samarium, europium, gadolinium, terbium, dysprosium, holmium, erbium,thulium, and ytterbium, display long-lived fluorescence (also referredto as phosphorescence or luminescence) with lifetimes ranging from about0.03 μs or less to about 1500 μs or more.

Measurement of the effective baseline shift produced by the long-livedfluorescent or luminescent compound, as compared to the baseline in theabsence of the compound, allows the determination of the presence and,if present, of the amount of the long-lived compound. A long-livedcompound can be used either in isolation or in addition to shorter-livedfluorescent species. FIGS. 21A and 21B illustrate two pedagogicalexamples of embodiments according to the present disclosure. In FIG.21A, the sample under measurement comprises one or more fluorophoreswhose longest fluorescence lifetime τ₁ is substantially shorter than theinterpulse period (or spacing or an excitation pulse repetition period)Δt₁. As a result, each of the interactions 2122 between an excitationlaser pulse and the fluorophore(s) in the sample presents a fluorescenceemission intensity I that rises to an approximate level I₁, thensubstantially decays to a zero baseline (represented here by the timeabscissa t) before the next pulse. The fluorescence emission signal 2120therefore comprises a series of approximately similar peaks spacedaccording to the interpulse period or an excitation pulse repetitionperiod Δt₁. In one embodiment, factors affecting the shape and magnitudeof signal 2120 are accounted for in a theoretical, mathematical,analytical, numerical, computational, and/or empirical model, anddesired measurands (such as, without limitation, the fluorescencelifetime values of one or more fluorophores, the contributions of one ormore fluorophores to the observed fluorescence emission intensitysignal, and/or the number and concentration of fluorophores in thesample under interrogation) are extracted, e.g., by fitting to theobserved signal, or quantities derived from it, a model in a processsimilar to that further described below.

In FIG. 21B, the sample under measurement comprises one or morefluorophores, one of which has a fluorescence lifetime Δt₂ which iscomparable to or longer than the interpulse period (or spacing or anexcitation pulse repetition period) Δt₂. For illustrative purposes,dotted curve 2132 indicates what the approximate shape of a singleinteraction between a laser pulse and the fluorophore(s) in the samplewould be if the interpulse spacing were much longer than the fluorophorelifetime τ₂. With the interpulse spacing as indicated by Δt₂, the decayof fluorescence from the fluorophore(s) in the sample does not haveenough time to reach zero baseline (represented here by the timeabscissa t) before the next excitation pulse in the train arrives. As aresult, the fluorescence emission signal 2130 builds up over time as aseries of ratcheting or incremental steps 2134 over an increasingbaseline 2136. Over the course of a sufficient number of excitationpulses (which number depends on factors including, without limitation,the intensity of the pulses, the duration of the pulses, the interpulsespacing, the number and concentration of absorbing fluorophores in theinterrogation volume, and/or the fluorescence lifetime of thefluorophore), the baseline 2136 shifts asymptotically to a newsteady-state level I₂. This baseline shift I₂ is measured and reportedand/or used to calculate the number or concentration of the fluorophorespresent in the sample.

The pedagogical examples of FIGS. 21A and 21B are chosen to illustratewith clarity certain principles of the present disclosure. It will beapparent to those skilled in the art that a wide range of differentconfigurations and outcomes are possible which are encompassed by thisdisclosure, even when they are not explicitly illustrated or described.For example, FIGS. 21A and 21B show schematically idealized fluorescenceemission signals expected from certain limited intervals of interactionbetween an excitation pulse train and a sample comprising one or morefluorophores. FIG. 21A illustrates a portion of approximate fluorescenceemission behavior resulting from a substantially constant interactionbetween the pulse train and, e.g., a particle in the sample. In thecourse of a typical such interaction, the magnitude of the emissiongenerally rises and falls as the particle traverses the interrogatinglight beam, as shown schematically, e.g., by envelope 1115 in FIGS.11A-11D. Over a suitably short period of time, particularly near thepeak of the envelope, the magnitude of the interaction can beapproximately constant as suggested by intensity I₁ in FIG. 21A,depending on factors such as, without limitation, the interpulsespacing, the beam waist dimensions, and/or the sample stream flow speed.Over the entire length course of the interaction, the shape of theinterrogating beam profile generally causes the level I₁ in FIG. 21A torise and fall similarly to envelope 1115 in FIGS. 11A-11D rather thanremaining approximately constant.

Similarly, in FIG. 21B a portion of an idealized fluorescence emissionsignal resulting from an interaction between an excitation pulse trainand, e.g., a particle in a sample is schematically illustrated for acase where the beam profile in the direction of particle and fluid flowcomprises a rising-edge step function and a plateau. In otherembodiments, the beam profile in the direction of particle and fluidflow follows a more gradual pattern, as shown schematically, e.g., byenvelope 1115 in FIGS. 11A-11D. In such embodiments, the shape of thefluorescence emission signal 2130 generally rises and falls as theparticle traverses the interrogating light beam, beginning withrelatively smaller ratcheting or incremental steps 2134 that increase inmagnitude as the envelope 1115 increases, reaching a peak (analogous tolevel I₂ in FIG. 21B), then decrease as the envelope 1115 decreases,eventually returning to the original baseline (represented here by thetime abscissa t).

The change in baseline 2136 over time is generally the result of anumber of factors, such as, without limitation, the intensity of thepulses, the duration of the pulses, the interpulse spacing (or anexcitation pulse repetition period), the beam waist dimensions, the beamprofile, the sample stream flow speed, the number and concentration ofabsorbing fluorophores in the interrogation volume, and/or thefluorescence lifetime of the fluorophores. As disclosed herein, thebaseline shift may comprise a temporary plateau, as illustrated in FIG.21B, or it may comprise an envelope with a well-defined peak, asdiscussed above. The terms “baseline shift” (i.e., the value of theplateau) and “peak baseline shift” (i.e., the value of the peak of theenvelope) are therefore considered interchangeable within this context,indicating, in both cases, the maximum shift in baseline due to thepresence of a fluorescent species having a lifetime comparable to orlonger than the interpulse period. The baseline subtraction may beperformed on the entire supercurve using an average or a peak value ofthe baseline shift, or it may be performed on a point-by-point basis,with each sampling point having an amount subtracted from itcorresponding to the local value of the baseline shift.

The baseline shift measurement described herein can be combined with thefluorescence lifetime decay analysis also described herein. For example,a sample may comprise two or more fluorophores, one of which has afluorescence lifetime τ₂ comparable to or longer than the interpulsespacing Δt₂, and another or other fluorophores which have fluorescencelifetimes shorter, or substantially shorter, than the interpulse spacingΔt₂. The fluorescence lifetime decay analysis described herein can insuch cases be applied to the fluorescence decays in the ratcheting orincremental steps 2134. In one embodiment, the shifting baseline 2136,as described in connection to FIGS. 21A-21B, is subtracted from themeasured fluorescence emission intensity signal 2130, and thefluorescence decay analysis described herein, e.g., in relationship toFIGS. 4A-D, 11A-11F, 12A-B, 18A-C, and 20A-C, is applied to theresulting baseline-subtracted signal. In another embodiment, factorsaffecting the shifting baseline 2136 are accounted for in a theoretical,mathematical, analytical, numerical, computational, and/or empiricalmodel along with factors affecting fluorescence decay in the individualratcheting or incremental steps 2134, and desired measurands (such as,without limitation, the fluorescence lifetime values of one or morefluorophores, the contributions of one or more fluorophores to theobserved fluorescence emission intensity signal, and/or the number andconcentration of fluorophores in the sample under interrogation) areextracted by fitting to the observed signal 2130, or quantities derivedfrom it, the model in a process similar to that further described below.A longer-lived fluorophore responsible for the baseline shift describedherein may be a known fluorophore, chosen by design and for which thedisclosed apparatus and methods are optimized; or it may be an unknownfluorophore, the presence and amount (and optionally the identity) ofwhich are detected by the disclosed apparatus and methods.

In practice it may be desirable to implement less than the theoreticalmaximum number of multiplexed combinations available by application ofthe present disclosure. Some of the practical reasons that may factorinto the criteria for such a choice (which may be hard coded duringdesign, or may alternately be left up to the instrument operator) mayinclude: the desire to reduce the computational complexity required fora full implementation of the possible combinations; the desire to reducethe computational time required to perform a statistically acceptableanalysis on the number of possible combinations; the desire tomanufacture or to obtain a simpler, smaller, less costly instrument thanwould be needed for a full implementation of the theoretical maximumnumber of possible combinations; the desire for an operator to be ableto operate the analytical platform with a minimum of specializedtraining; and the desire for a robust instrument designed to perform areduced set of operations in a highly optimized fashion. Whichever themotivation, one may choose to produce a “sparse” multiplexedconfiguration, where some of the possible multiplexing choices have beenremoved.

In one embodiment of the present disclosure, such sparseness isintroduced in the lifetime domain: Only a few of the possible lifetimebins are provided, the rest being removed and being replaced by gapsbetween the provided lifetime bins [e.g., removing bins τ₂ (522) and τ₄(524) in FIG. 5]. The advantage of this configuration over a denselypopulated lifetime configuration is that the relative sparseness of thelifetime bins simplifies the process of digitally distinguishing thelifetime contributions of the remaining bins to the optical emissionsignal.

In another embodiment of the present disclosure, the sparseness ofmultiplexing is introduced in the spectral domain: Only a few of thepossible wavelength bands are provided, the rest being removed and beingreplaced by gaps between the provided spectral bands [e.g., removingbands FL₂ (512) and FL₄ (514) in FIG. 5]. The advantage of thisconfiguration over a densely populated spectral configuration is thatthe relative sparseness of the spectral bands simplifies the handling ofany residual spectral overlap.

FIG. 6 shows an illustrative example (graph 600) of yet anotherembodiment of the present disclosure, where the sparseness ofmultiplexing has been introduced in both the spectral (graph 650 atbottom) and the lifetime domains simultaneously: Only a few of thepossible wavelength bands have been provided, the rest having beenremoved and being indicated in the figure by the gaps between theprovided spectral bands [e.g., between bands FL₁ (611) and FL₃ (613) andbetween bands FL₃ (613) and FL₅ (615)]; and only a few of the possiblelifetime bins have been provided, the rest having been removed and beingindicated in the figure by the gaps between the provided lifetime bins[e.g., between bins τ₁ (621) and τ₃ (623) and between bins τ₃ (623) andτ₅ (625)]. The resulting configuration, while having considerably fewerintersection points than the theoretical maximum, is however advantagedover a densely populated spectral and lifetime configuration by thereduction in the number of hardware components, the reduction in thecomplexity of the signal processing algorithms, by the relative increasein robustness and accuracy of the signal processing results, and by therelative simplicity of a training protocol for operation of theassociated instrument platform.

FIG. 7 illustrates schematically a system configuration of an exemplaryembodiment of the present disclosure, which provides an apparatus forhighly multiplexed, compensation-reduced, and/orautofluorescence-interference-reduced particle analysis in a sample. Inanother embodiment, it provides an apparatus for lifetime analysis ofparticles in a sample. One or more light source 750, e.g., a laser,produces one or more optical energy (light) beams 722 with desiredwavelength, power, dimensions, and cross-sectional characteristics. Oneor more modulation drivers 752 provide modulation signal(s) 702 for theone or more respective light sources, resulting in the beam(s) 722becoming pulsed. The modulation drivers may optionally be internal tothe light source(s). The pulsed beam(s) are directed to a set of relayoptics 754 (which can include, without limitation, lenses, mirrors,prisms, and/or optical fibers), which may additionally optionallyperform a beam-shaping function. Here relay optics will be intended torepresent means to transmit one or more beams from one point in thesystem to another, and will also be intended to represent means to shapeone or more beams in terms of dimensions and convergence, divergence, orcollimation. The output pulsed beam(s) 732 from the beam-shaping relayoptics are directed to another optional set of relay optics 758 (whichcan include, without limitation, lenses, mirrors, prisms, and/or opticalfibers), which may additionally optionally perform a focusing function.The beam-shaping optics, the focusing optics, or both, may alternativelybe incorporated into the light source module. The combined effect of thetwo sets of relay optics (the beam-shaping and the focusing sets) uponthe input beam(s) from the light source(s) is to impart upon the beam(s)the desired output beam propagation characteristics suitable forinterrogating particles, such as, without limitation, a desired focusedbeam waist, a desired ellipticity, a desired polarization, and/ordesired wavefront characteristics, each on or about, e.g., theinterrogation plane. The second set of relay optics then directs thepulsed beam(s) 708 to the flowcell 700. The flowcell 700 provides forthe passage of particles to be analyzed (which can include, withoutlimitation, cells, cell aggregates, bacteria, viruses, exosomes,liposomes, microvesicles, microparticles, nanoparticles, natural orsynthetic microspheres, and chemical or biochemical molecules orcompounds) by conveying a sample stream 740 containing said particles asa suspension, and a stream of sheath fluid 742 that surrounds andconfines said sample stream, as further described herein. An inputportion of the flowcell focuses, e.g., by hydrodynamic means, the samplestream and the surrounding sheath stream to result in a tight samplecore stream flowing through a microchannel portion of the flowcell,surrounded by sheath fluid. The tight sample core stream flowing pastthe interrogation region of the flowcell typically exposes, on average,less than one particle at a time to the beam or beams for interrogation(this is sometimes referred to in the art as “single-file” particleinterrogation even in those cases when the particles may not besubstantially in single file, but may nevertheless generally pass one ata time through the interrogation region). The sheath fluid and thesample core stream are directed to a single outlet 744 (and generallydiscarded as waste) after passage through the interrogation portion ofthe flowcell. As the interrogating pulsed beam(s) of optical energy(light) interact with particles in the sample core stream by scattering,absorption, fluorescence, and other means, optical signals 710 aregenerated. These optical signals are collected by relay optics in box760 (which can include, without limitation, single lenses, doubletlenses, multi-lens elements, mirrors, prisms, optical fibers, and/orwaveguides) positioned around the flowcell, then conveyed to filteringoptics in box 760 (which can include, without limitation, coloredfilters, dichroic filters, dichroic beamsplitters, bandpass filters,longpass filters, shortpass filters, multiband filters, diffractiongratings, prisms, and/or holographic optical elements) and then conveyedas filtered light signals 712 by further relay optics in box 760 to oneor more detectors 770 (which can include, without limitation,photodiodes, avalanche photodiodes, photomultiplier tubes, siliconphotomultipliers, avalanche photodiode microcell arrays, photodiodearrays, avalanche photodiode arrays, photomultiplier tube arrays,silicon photomultiplier arrays, and arrays of avalanche photodiodemicrocell arrays). The detectors convert the optical signals 712 intoelectronic signals 772, which are optionally further amplified andgroomed to reduce the impact of unwanted noise. The electronic signalsare sent to an electronic signal processing unit 790 [which generallycomprises a digitization front end with an analog-to-digital converterfor each signal stream, as well as discrete analog and digital filterunits, and may comprise one or more of a Field-Programmable Gate Array(FPGA) chip or module; a Digital Signal Processing (DSP) chip or module;an Application-Specific Integrated Circuit (ASIC) chip or module; asingle-core or multi-core Central Processing Unit (CPU); amicroprocessor; a microcontroller; a standalone computer; and a remoteprocessor located on a “digital cloud”-based server and accessed throughdata network or wired or cellular telephony means], which executesfurther processing steps upon the electronic signals. The processedsignals 774 are then sent to a data storage unit 792 (which can include,without limitation, a read-only memory unit, a flash memory unit, ahard-disk drive, an optical storage unit, an external storage unit, or aremote or virtual storage unit connected to the instrument by means of awired data or telecommunication network, a Wi-Fi link, an infraredcommunication link, and/or a cellular telephony network link). Thestored or preliminarily processed data, or both, can also be madeavailable to an operator for optional inspection of results.

FIG. 8 illustrates schematically a system configuration of an exemplaryembodiment of the present disclosure, which provides an apparatus forhighly multiplexed, compensation-reduced, and/orautofluorescence-interference-reduced analysis and sorting of particlesin a sample. In another embodiment, it provides an apparatus forlifetime analysis and sorting of particles in a sample. It is similar inconfiguration to the system configuration of FIG. 7, except in that itadditionally provides for the capability to sort and collect particlesbased on their characteristics. The signal processing unit 890 generatesin real time sorting control signals 876 based on the presence orabsence or degree or nature of predetermined characteristics of theparticles to be analyzed. For example, it may be desirable to identifyand sort particles that, upon excitation by the interrogating pulsedlight beam(s), emit fluorescence in a predefined spectral band at alevel above a predefined threshold. As another example, it may bedesirable to identify and sort particles that, upon excitation by theinterrogating pulsed beam(s), exhibit fluorescence decay curve with alifetime component in a certain range of values and at a percentageabove a predefined threshold. In one embodiment, the predefined spectralband, range of lifetime values, and/or predefined threshold maycorrespond to a lifetime component attributable to autofluorescentmolecules. Different criteria may be used in isolation or combined incompound logical forms (such as AND, OR, NOT, as well as more complexforms involving numerical comparisons of different quantities, such as,without limitation, “greater than,” “less than,” and so forth). Theprocessing unit 890, once the processed signals from a given particlemeet the predefined set of sorting criteria, triggers a signal 876conveyed to an actuator driver 894. The actuator driver is an electroniccontrol module connected to one or more sorting actuators 880. Thesorting actuators may be positioned in, on, next to, or near theflowcell in the vicinity of, and downstream from, the interrogationregion. One or more of the sorting actuators 880 is temporarilyactivated by drive signal 878 from the actuator driver 894 in responseto the triggering signal 876 from the processing unit 890, resulting ina temporary diversion of the sample core stream, or of a portion of thesample core stream, away from the default (or waste) sorting channel 846and into one or more sorting channels 848. The default sorting channel846 optionally directs the fluids it receives into a default (or waste)receptacle 847. The sorting channel(s) 848 direct the sample corestream, in turn, to respective receiving sorting receptacle(s) 849. Oncethe temporary activation of one or more of the sorting actuators 880 iscomplete, the actuator(s) return to their resting state, and the samplecore stream returns to its default (or waste) sorting channel 846. Thesorting actuator(s) 880 are controllable to achieve multiple actuationstates, including, without limitation, with an actuator driver 894, witha built-in control, with direct voltage or current control from theprocessing unit 890, or with electrical signals coming directly fromlogic circuitry connected with the one or more detectors 870.

In FIGS. 9 and 10, the relative orientation of fluid flow, lightpropagation, and transverse directions is shown, respectively, as theset of axes x, z, and y. The process steps involved in the performanceof some embodiments of the present disclosure are described here inreference to FIGS. 9 and 10, and are also further summarized inflow-chart fashion in FIG. 18A.

FIG. 9 illustrates a cross-section, perpendicular to the direction offluid flow, of a possible light collection configuration of the presentdisclosure. A flowcell 900, of which the inner part is schematicallyindicated in the figure, provides a channel for fluid flow. Sheath fluid920 is provided to confine the fluid 930 carrying particles 955 to beanalyzed. The sheath fluid and the sample-carrying fluid are focusedinto the flowcell lumen, optionally by hydrodynamic means; such focusingproduces a tight sample core stream bounded by the sheath fluid. Aninterrogating light beam or beams 940 are provided to interact with theparticles in the sample core stream. The beam or beams, usually having aGaussian intensity profile, are generally focused into a relativelytight spot in the plane of the sample core stream. Particles to beanalyzed in the sample core stream interact with light in the beam orbeams 940 to generate optical signals 910 by optical processesincluding, for instance, scattering, absorption, or fluorescence. Theoptical signals 910 are collected by collection optics 960. Thecollected optical signals 912 are then conveyed (relayed) to spectralfiltering optics 962 to select appropriate spectral bands of the opticalsignals for detection. The spectral filtering optics 962 may include,without limitation, reflective, transmissive, absorptive, diffractive,or holographic means, or means based on interference, or a combinationthereof. The resulting spectrally filtered optical signals 914 are thenconveyed (relayed) as signals 916 by focusing optics 964 to a detector970. The detector converts the light signals 916 into electrical signals972, which are then conveyed to a processing unit 990 for furtheranalysis, processing, and optionally storage, as described above inreference to FIGS. 7 and 8 and as further discussed below. Together, thecollection optics 960 and the focusing optics 964 may be referred to asrelay optics.

In some embodiments, more than one spectral band output may begenerated. For instance, FIG. 10 illustrates a cross-section,perpendicular to the direction of fluid flow, of another possible lightcollection configuration of the present disclosure. It is similar inconcept to the configuration illustrated in FIG. 9 except that thespectral filtering optics 1062 produce more than one spectral bandoutput 1014 (A and B), separated according to spectral characteristics.Each spectral band is then conveyed (relayed) to a separate set offocusing optics 1064 (A and B) and separate detectors 1070 (A and B),resulting in respectively separate electrically converted signals 1072(A and B). The resulting electrical signals are then routed to signalprocessing unit 1090 for further elaboration. FIG. 10 depicts, for thesake of clarity, two sets of spectral bands, focusing optics, anddetectors; it will be apparent to those skilled in the art that anarbitrary number of such sets is encompassed by the scope of the presentdisclosure.

The process steps described below in conjunction with FIGS. 11A-11F, 12Aand 12B are also summarized in a flow-chart fashion in FIG. 18C.

FIGS. 11A-11F illustrate, for the specific case of implementation of thepresent disclosure on a flow cytometry platform, the principles involvedin excitation, emission, detection, and analysis of fluorescencelifetime signals from particles under analysis. The various panels ofthe figure will be referred to in the text that follows to betterillustrate the various steps of the signal transduction process. In eachpanel (A)-(F), a graph depicts the evolution over time (t) of certainoptical intensities (I). In every case except where noted, the opticalintensities are each shown as normalized to unit peak values for clarityof illustration; normalization of the optical intensities is preferablein certain embodiments, while in certain other embodiments the opticalintensities are not normalized.

The graph 1110 in FIG. 11A depicts the canonical behavior of the opticalsignals resulting from the interaction between an always-on excitationlight source (also referred to in the art as a constant-wave, or cw,source) and a particle passing through the region of interrogation(typically in a flow cell or other component having a similar function).As the particle enters, then exits, the region of interrogation, theexcitation interaction signal (the dash-dotted line 1115) rises thenfalls, in concert with the spatial profile of the light beam used forinterrogation, as measured along the line of passage of the particle.The particle, in this canonical pedagogical illustration, is assumedsmall in comparison with the dimension of the light beam along thedirection of passage of the particle; modifications to this frameworkthat generalize this to the case of particles of arbitrary size arepossible but are not informative for the purpose at hand, and are nottaken up here. For a typical light beam having dimensions, along theline of passage of particles to be analyzed, from less than 10 to morethan 100 microns, and a typical flow cytometer causing particles to passthrough the region of interrogation at flow velocities of below 0.1 tomore than 10 m/s, the range of possible durations of the excitationinteraction envelopes, as the shape of curve 1115 in FIG. 11A issometimes referred to, is quite wide, stretching from less than 1 μs tomore than 1 ms. However, in most cases in current practice thefull-width at half-maximum (FWHM) of the excitation interaction envelopeis from around a few microseconds to around a few tens of microseconds.

The graph 1120 in FIG. 11B juxtaposes, for illustrative purposes, thecanonical interaction envelope 1115 from FIG. 11A (the dash-dotted line)with one possible configuration of excitation pulses from a modulatedsource of optical energy. The pulses are shown in a train of uniformlyrepeated, substantially identical units (the sharp features 1122 inthick solid lines); each pulse is short as compared to the FWHM of thecanonical interaction envelope, and each pulse is separated fromneighboring pulses by a time generally larger than the width of thepulses themselves. One key aspect of the present disclosure in FIG. 11Bis that the modulation of the optical energy source (or sources) shouldresult in a series of substantially identical pulses, each shortcompared to the typical interaction time, and each well separated fromthe next.

The graph 1130 in FIG. 11C depicts the prophetic result of deliveringthe train of excitation pulses 1125 illustrated in FIG. 11B to aparticle flowing in a flow cell according to design and operatingparameters typical of flow cytometer constructions known in the art. Theresulting excitation interactions are shown as a series of pulses ofvarying height (features 1132 in thick solid lines) conforming to anoverall envelope (the dash-dotted line), said envelope corresponding tothe interaction envelope that would result, were the light beamcontinuous instead of pulsed and all other things remaining equal. Whilethe details of the interaction sequence would, generally, vary fromparticle to particle (for example, the detailed timewise location of theindividual interaction pulses 1132 in FIG. 11C under the overallenvelope is a function of the relative timing of the pulse train withrespect to the arrival of the particle), the general nature of theexcitation interaction as consisting of a series of pulses modulated bya “carrier” envelope is determined by the design and operatingparameters of the apparatus. In this graph 1130 of FIG. 11C, theindividual interaction pulses are not normalized to unit intensity. Andwhile in FIG. 11C are shown only six interaction pulses 1132 for clarityof illustration, it will be recognized by those skilled in the art thatthe present disclosure is not limited to such a number, the number ofpossible interaction pulses encompassed by the present disclosure beingas small as one and as large as thousands or larger.

The graph 1140 in FIG. 11D adds another key element of the currentdisclosure to the picture, namely the ability to measure the temporalevolution of the fluorescence decay curves. The overall carrier envelope(dash-dotted line) and the individual excitation interaction pulses(features in thick solid lines) are as illustrated in FIG. 11C. Thefluorescence decay curves are shown as thin dashed lines 1147. Eachfluorescence decay curve follows directly the optical excitationassociated with the interaction pulse immediately preceding it. It canbe appreciated that the fluorescence decay curves are, generally,asymmetric: While the rising portion is dominated by the process ofabsorption of optical energy from the excitation source, the waningportion (the decay) is driven by the quantum mechanical processes offluorescence emission, which vary from molecule to molecule and mayadditionally be affected by the molecular microenvironment, andgenerally result in a curve with a longer decay-side tail than theabsorption-side tail. In this graph 1140 of FIG. 11D the individualinteraction pulses and the individual fluorescence decay curves are notnormalized to unit intensity.

The next process step in the lifetime analysis algorithm involvessegmenting the signal sequence into individual decay curves. The dashedcurves 1147 in graph 1140 of FIG. 11D represent optical emissionsignals, such as, e.g., fluorescence decay curves; these optical signalsare detected by one or more detectors, converted into electrical signal,and digitized for further processing. In graph 1150 of FIG. 11E, thesequence of pulse signals (dashed curves, representing digitizedelectrical signals corresponding to the optical signals they areconverted from) is broken (e.g., electronically, mathematically,numerically, digitally, computationally, or algorithmically) intoindividual pulse signal segments 1151-1156 (A, B, C, . . . ) whilemaintaining a consistent phase across the entire sequence; that is, aselected feature of each pulse (e.g., the peak, the midpoint of itsrising edge, etc.) is chosen as the reference, and the sequence is cutup into equal segments (shown below the axis in FIG. 11E), allconsisting of substantially the same number of digitization elements(also referred to as sampling points), and all starting substantiallythe same number L of digitization elements to the left of the respectivereference point, such number L being chosen to result in segments, eachof which (whenever possible) contains an entire decay curve not splitbetween adjacent segments, as illustrated schematically in FIG. 11E. Thesegment length 1167 is chosen to closely match excitation pulse spacing1166.

In an alternate embodiment, the reference points used to cut up thefluorescence signal sequence into substantially equal segments are drawnfrom a reference electronic signal derived from sources that include,without limitation: (i) the sequence of external pulses from amodulation device used to modulate the light source; (ii) a low-jitter,synchronized output function from the modulation device that has thesame repetition frequency as the modulation pulse train; (iii) alow-jitter, synchronized output function from the internally modulatedlight source that has the same repetition frequency as the modulatedpulse train; (iv) a low-jitter, synchronized output function from theexternally modulated light source that has the same repetition frequencyas the modulation pulse train; (v) the output of a fast photodetectordesigned to collect light from the excitation pulse train, e.g., as a“tap” on the main excitation beam, from surface scattering on, orpartial reflections from, one or more optical components in the opticalpath of the excitation beam; and (vi) the output of a fastforward-scatter detector, a fast small-angle scatter detector, a fastintermediate-angle scatter detector, a fast side scatter detector, afast backscatter detector, or generally any fast photodetector(including, without limitation, photomultiplier tubes, siliconphotomultipliers, photodiodes, and avalanche photodiodes) arranged todetect excitation light scattered elastically by particles in thesample, and optionally designed to reject light inelastically scatteredor converted by fluorescence processes; where “fast” means having theability to convert detected light signals into electrical signals withsufficient bandwidth and at sufficient speed to minimize distortion,broadening, and other artifacts in the optoelectronic conversion processand, optionally, the preamplification and amplification processes. Forexample, for optical pulses between 0.5 and 10 nanoseconds in duration,it is preferable to have a bandwidth greater than 100 MHz, morepreferable to have a bandwidth greater than 250 MHz, and most preferableto have a bandwidth greater than 2 GHz; other choices of bandwidth arealso possible and may be desirable, depending on factors including,without limitation, component cost, the electronic noise of thedetector/preamplifier at the chosen bandwidth, the degree of jitterpresent on the signal from other sources, and the electroniccharacteristics of other components in the signaldetection/amplification/processing path. The reference electronic signalthus obtained provides a sequence of pulses, from each of which aselected feature (such as, e.g., the peak, the midpoint of the risingedge, etc.) is used as the reference for the demarcation of segmentboundaries in the simultaneously collected fluorescence signal depictedin FIG. 11E.

Graph 1160 in FIG. 11F depicts the following step of signal processingby showing each of the decay curve segments 1151-1156 from FIG. 11E (A,B, C, . . . ) added coherently on top of each other, i.e., with therespective temporal relationships within each segment unchanged. Suchadding is performed coherently on the basis of individual digitizationelements. For example, each digitization index may be represented as aninteger (e.g., 1, 2, 3, etc.). The values corresponding to the firstdigitization index (#1) in every segment are added together (A1+B1+C1 .. . ), the values corresponding to the second digitization index (#2) inevery segment are added together (A2+B2+C2 . . . ), and so on forsubstantially all digitization indices in all segments. The result is a“supercurve” (bold curve 1165 in FIG. 11F), where each digitizationindex (e.g., #1, #2, #3, etc.) corresponds to a value that is equal tothe sum of all the signal values (e.g., A1+B1+C1, A2+B2+C2, A3+B3+C3,etc.) from the corresponding digitization indices from all segments. Thesupercurve is then optionally converted to a semilog scale for furtherprocessing.

This signal processing method removes incoherent noise contributionsfrom the result while boosting the contribution of signals coherent frompulse to pulse. The supercurve 1165 in graph 1160 of FIG. 11F may stillexhibit some degree of incoherent noise, which is to be expected giventhe stochastic nature of the decay process and the presence of varioussources of measurement noise on the signals; however, the general natureof the decay is expected to remain constant within a given population offluorophores, and the supercurve process is aimed at maximizing thesignal from such common decay while minimizing the effect of stray lightsignals, electronic noise, and other events lacking information contentgermane to the analysis being carried out.

FIGS. 12A and 12B illustrate exemplary embodiments of several steps ofan analysis method of the current disclosure. Both FIGS. 12A and 12Bdisplay curves plotted on a semilog scale of the natural (or,alternatively, the base-10) logarithm axis of measured intensity vs. thelinear axis of time. In graph 1250 of FIG. 12A the excitation pulse(bold solid curve 1201) and the resulting emission supercurve due, e.g.,to fluorescence (dashed curve 1210) are each shown as normalized tounity peak value for clarity of illustration; on the shown logarithmicscale, a linear value of one corresponds to the logarithmic value ofzero. Normalization of the emission supercurve is preferable in certainembodiments, while in certain other embodiments the supercurve is notnormalized. The supercurve 1210 shown is obtained as described above inreference to FIGS. 11A-F for supercurve 1165. For illustrative purposes,the supercurve 1210 in graph 1250 of FIG. 12A is shown as comprisingthree distinct lifetime components [each also referred to herein as“component,” “lifetime,” “lifetime value,” “1/e value,” “time constant,”“decay constant,” or “exponential decay”, and corresponding to the valueof τ in the standard exponential decay formula I(t)=I₀ exp(−t/τ), whereI₀ is the starting intensity and I(t) is the intensity after a time t]:τ_(a), τ_(b), and τ_(c). In this example, τ_(a) is the smallest timeconstant of the three, m is the largest, and m is intermediate betweenthe two. The relative values of τ_(a), τ_(b), and τ_(c) are reflected inthe slopes of the three branches a (1221), b (1222), and c (1223) of thesupercurve 1210: the slope of branch a 1221 is steepest, the slope ofbranch c 1223 is mildest, and the slope of branch b 1222 is intermediatebetween the two. The slope of a branch on a semilog plot of the kinddepicted in FIGS. 12A and 12B is inversely proportional to the value ofthe corresponding time constant. The three branches 1221-1223 of thesupercurve 1210 in FIG. 12A are defined as follows: The first branch a1221 begins at or near the peak 1231 of the supercurve and ends at ornear the first “knee” 1232 of the supercurve (where by “knee” is meant asubstantial change in slope, indicated by an open circle); the secondbranch b 1222 begins at or near the first knee 1232 and ends at or nearthe next knee (the next open circle 1233); the third branch c 1223begins at or near this next knee 1233 and ends at or near 1234 where thesupercurve meets the measurement noise floor (schematically indicated inFIG. 12A by the time axis t). The slope of each branch is defined ascustomary as the ratio of the ordinate and the abscissa over a portionof or the entire branch: e.g., for branch b, the slope value iss_(b)=y_(b)/t_(b). The time constant corresponding to such slope is thenobtained by the reciprocal of the slope: τ_(b)=1/s_(b).

It will be appreciated that when dealing with real measurements subjectto effects including, without limitation, noise, background,uncertainty, instrument error or drift, component variability, and/orenvironmental effects, there may be departures, sometimes substantial,from the illustrations and depictions presented here. Even when sucheffects are low or minimized, other effects may act to mask, distort,alter, modify, or otherwise change the relationships among the variousmathematical and physical quantities mentioned here. As one example, thenoise floor where the supercurve in FIG. 12A starts and ends may behigher in certain cases and lower in others, depending on severalfactors, including, without limitation, the ones just mentioned. Thevariability in the noise floor may affect the determination of one ormore of the slopes of the branches of the supercurve. Likewise, theprecise location of a knee between two branches on the supercurve may besubject to uncertainty depending on the level of residual noise on thesupercurve. Depending on the digitization sampling rate and on the noisepresent on the signal, the transition from one slope to the next (theknee) may occur more gradually than over a single data point, forexample over two, three, or more data points. Another example of thedistortion created by physical effects is shown in FIG. 12A where brancha 1221 is shown as beginning at the peak 1231 of the supercurve 1210,however the slope of this first branch does not immediately convergeonto a stable value due to the roll-off from the peak. The degree ofroll-off is dependent on the shape of the excitation pulse, the value ofthe first-branch lifetime, and other factors. These effectsnotwithstanding, one improvement of the present disclosure is used tominimize the impact of such effects. Construction of the supercurve froma number of individual pulse signals, with its attendant improvement insignal to noise, is one element that contributes to such minimization.

Another element is the relative simplicity in the extraction of desiredparameters, such as the values of the time constants, from a supercurve.This is illustrated by graph 1280 in FIG. 12B. Graph 1280 shows a detail1211 of the supercurve 1210 of FIG. 12A, where branch a 1281(corresponding to branch a 1221 in graph 1250) ends and branch b 1282(corresponding to branch b 1222 in graph 1250) begins. (The graph hasbeen offset and rescaled in both abscissa and ordinate for illustrativepurposes.) The two branches meet at the knee 1292 indicated by the opencircle, corresponding to knee 1232 in graph 1250. Also plotted in FIG.12B are the individual digitized points (also referred to as digitalsamplings) of the supercurve, indicated by small filled circles withsolid drop lines to the time axis. The process step of determining thelocation of a knee (that is, the transition between one branch where avalue of lifetime dominates, to another branch where a different valueof lifetime dominates) comprises computing differences betweensuccessive values of the digitized supercurve. Four such difference forbranch a are shown as δ_(a). Where residual noise on the supercurve isminimized, the value of δ_(a) from digitized point pair to digitizedpoint pair will show little variation. Once the knee is crossed,however, the next computed difference will jump to δ_(b), and successivedifferences will once again remain substantially uniform around this newvalue. For one of the main improvements of the present disclosure,namely the provision of highly multiplexed means of particle analysisand sorting, it is not critical that the depicted successive values ofδ_(a) be rigorously constant, nor those of δ_(b); it is merelysufficient that δ_(a) be different enough from δ_(b) to enable detectionof the slope change at, or within a reasonably narrow range of, theindicated knee point. Sufficient difference between δ_(a) and δ_(b) isrelated to the precision and accuracy of the measurement system, thenumber, types, and severity of noise or error sources, and otherfactors. Detection of a discontinuous change in slope, however, isintrinsically simpler, instrumentally and computationally, than theabsolute determination of the value of a slope.

Once a knee is found, the process continues until the entire supercurveis examined. The location of each knee, together with the location ofthe start of the first branch and the end of the last branch, define allthe branches of the supercurve. The next processing step involvescomputing the average slope for each branch, which was described abovein reference to FIG. 12A, and from such slope values the time constantsof each branch are calculated. The following processing step involvesallocating each branch to one of a set of predetermined lifetime (ortime constant) bins. As illustrated in FIGS. 5 and 6, one aspect of thepresent disclosure is the provision of a limited set of lifetime bins,where the lifetime within any one bin is allowed to vary somewhat, aslong as the variation is not greater than the difference betweenneighboring bins. For the purpose of analyzing a supercurve anddetermining what lifetimes gave rise to the signals from which thesupercurve was constructed, it is sufficient to establish (1) which ofthe lifetime bins was present in the measured particle or event (i.e.,what fluorophores or other molecular species were present with afluorescence decay value within the range of any one of the providedlifetime bins), and (2) the degree of relative contribution of eachdetected lifetime. For (1), the set of time constants computed from thebranches of the supercurve as described above is compared to the set ofallowed lifetime bins. In some cases there will be as many separatedetected branches in a supercurve as there are bins: this would be thecase for FIG. 12A, for example, if the number of allowed bins were 3. Inother cases there will be fewer branches, indicating that a certain binwas not present (i.e., that no fluorophore or molecular species with alifetime in the range of values of that bin was detected). By comparingthe set of measured time constants with the set of allowed bins, adetermination is made as to which bins are present in the measurement.Determination of the relative contribution of each detected lifetime(now associated with one of the allowed lifetime bins) is performed bycomparing the values of the ordinates of each branch (the values y_(a),y_(b), and y_(c) in graph 1250 of FIG. 12A) with a calibration lookuptable generated during manufacture of the apparatus. Such calibrationlookup table is created by generating supercurves with known inputs,i.e., with 100% of one lifetime bin, 100% of another, and so on for allthe lifetime bins selected to be available on the apparatus; then withvarying mixtures of bins, such as, e.g., 10% of bin 1 and 90% of bin 2,20% of bin 1 and 80% of bin 2, and so on until 90% of bin 1 and 10% ofbin 2; repeating this for each pair of bins available on the apparatus.The resulting data provides the lookup table to compare measuredlifetime ordinates (e.g., y_(a), y_(b), and y_(c)) with, and therebydetermine, with interpolation if desired for greater accuracy, therelative contributions of each detected lifetime.

Another exemplary embodiment of the present disclosure involvesperforming curve fitting of a model (which can be, without limitation,mathematical, analytical, numerical, computational, empirical, or acombination thereof) to the constructed supercurve. The fittingprocedure (using methods that include, without limitation, linearregression, nonlinear regression, least squares fitting, nonlinear leastsquares fitting, partial least squares fitting, weighted fitting,constrained fitting, Levenberg-Marquardt algorithm, Bayesian analysis,principal component analysis, cluster analysis, support vector machines,neural networks, machine learning, deep learning, and/or any of a numberof other numerical optimization methods well known in the art) isdesigned to determine the most likely combination of lifetimes andcontributions resulting in the observed supercurve. In the case ofhighly multiplexed analysis and sorting of particles based on lifetimeanalysis, an instrument is generally designed with a fixed number ofknown, discrete lifetime bins. Therefore, the fitting procedure does notrequire the determination of the lifetimes, but simply of thecontributions of each lifetime component to the observed signal. Thismakes the fitting procedure much more constrained than would normally bethe case, and this in turn makes the determination of lifetimecontributions faster and computationally less expensive. In thisexemplary embodiment, extraction of slopes from the supercurve,identification of each knee present in the supercurve, and determinationof lifetime contributions from lookup tables are all replaced by thefitting procedure; the outcome of the fitting procedure is the best-fitset of contributions to the observed signals from each potentiallycontributing lifetime bin. In certain cases, the contributions from one,or more, or from all but one, or from all lifetime bins may bedetermined by fit to be zero or substantially zero; in certain cases,the fit may produce substantially nonzero contributions from one, frommore than one, or from all lifetime bins. In the case of an apparatusfor lifetime analysis of particles in a sample, where the lifetime orlifetimes are not known a priori, the fitting procedure would includedetermination of the lifetime values, as well as of the contributions ofeach lifetime to the observed signal.

Yet another embodiment of the present disclosure involves computing thearea under the supercurve, and comparing the result with one or morelookup tables. FIGS. 20A-20C illustrate this procedure for threeexemplary cases of a multiplexed system with two lifetime bins: (a) thecase of a supercurve with only a short-lifetime (τ_(a)) decay, (b) thecase of a supercurve with only a long-lifetime (τ_(b)) decay, and (c)the case of a supercurve with a two-lifetime (τ_(a) and τ_(b)) decay.Each of FIGS. 20A-20C displays curves plotted on a linear scale ofmeasured intensity I vs. the linear axis of time t. Each graph 2050,2051, and 2052 of FIGS. 20A-20C, respectively, shows the emissionsupercurve due, e.g., to fluorescence, as dashed curve 2010. The curvesin these graphs are shown after baseline correction (which step isdescribed below), and after normalization to unit peak height (whichstep is described below), for clarity of illustration; the raw measuredintensity supercurves can generally take on a range of values of bothbaseline and peak height. The area under each curve is shown as theshaded regions 2035, 2036, and 2037 in FIGS. 20A, B, and C,respectively. In these Figures, the area is accumulated starting fromthe time 2031 when the supercurve 2010 reaches its peak value, andcontinuing through the rest of the supercurve. In an alternativeembodiment, the entire area under the supercurve, including the unshadedarea under the supercurve before the time 2031 when the supercurvereaches its peak value, is computed. In another embodiment, the areaunder the supercurve is computed starting from a time subsequent to thestart of the supercurve but prior to the time 2031 when the supercurve2010 reaches its peak value, and continuing through the rest of thesupercurve. In yet another embodiment, the area under the supercurve iscomputed starting from a time subsequent to the time 2031 when thesupercurve 2010 reaches its peak value, and continuing through the restof the supercurve. In yet another embodiment, the area under thesupercurve is computed starting from a time prior to or subsequent tothe time 2031 when the supercurve 2010 reaches its peak value, andcontinuing to a second, later time prior to the end of the supercurve.

The area under the supercurve can be efficiently computed bysequentially adding successive measured intensity values of thesupercurve, i.e., the values corresponding to exemplary digitizedsampling points 2071 for the supercurve illustrated in FIG. 20A; thevalues corresponding to exemplary digitized sampling points 2073 for thesupercurve illustrated in FIG. 20B; and the values corresponding toexemplary digitized sampling points 2075 for the supercurve illustratedin FIG. 20C. While only sets of three digitized sampling points areshown illustratively in each Figure, it will be readily apparent tosomeone of ordinary skill in the art that a supercurve may comprise manysuch points, as schematically indicated by the ellipses in each Figure,such as, e.g., tens, hundreds, or more points. To increase the accuracyof the value of the area under the supercurve, standard methods ofnumerical integration as are well known in the art may be optionallyemployed, including, without limitation, the trapezoidal rule,quadrature, splines, and interpolation. In an embodiment, the supercurveis not baseline-corrected, i.e., the sum of intensity values is notadjusted by subtracting a baseline. In another embodiment, thesupercurve is baseline-corrected prior to the start of the areacomputation, i.e., a value corresponding to its baseline is subtractedfrom each measured intensity value to yield a supercurve with thecorrected, zero-baseline 2038 illustrated in FIG. 20A. In anotherembodiment, the supercurve is baseline-corrected after adding thedigitized sampling values together, i.e., a value corresponding to itsbaseline multiplied by the number of added digitized sampling values issubtracted from the sum of intensity values to yield a supercurve withthe corrected, zero-baseline 2038 illustrated in FIG. 20A. Baselinecorrection is performed using any of a number of methods well known inthe art, including filtering, signal conditioning, analog signalprocessing, digital signal processing, and hybrid signal processingmethods. In an embodiment, the sum of intensity values (whetherbaseline-corrected or not) is then optionally multiplied by the samplinginterval 2039 (δt) to yield the areas 2035, 2036, and 2037,respectively, in each of the exemplary cases illustrated in FIGS. 20A,B, and, C. In another embodiment, for greater computational efficiency,this step can be skipped, as long as the values in the lookup tablesthat the measured area is compared to are generated in an analogous way;to avoid confusion, in what follows we will refer to “area under thesupercurve” (alternatively, “supercurve area”) interchangeably as eitherthe sum of intensity values, or such sum multiplied by the samplinginterval 2039. The computed area under the supercurve is then normalizedby dividing it by the raw measured peak value reached at time 2031.

The computed normalized value of the area under the supercurve is thencompared with values in a lookup table constructed to cover a specifiedrange of possible supercurve area values that can be obtained in ameasurement. Such a lookup table includes relatively low values of thearea, such as area 2035 depicted in FIG. 20A; relatively high values ofthe area, such as area 2036 depicted in FIG. 20B; and relativelyintermediate values of the area, such as area 2037 depicted in FIG. 20C.For the exemplary case illustrated here of a multiplexed system with twolifetime bins, the lookup table furnishes, for each value of supercurvearea, a corresponding pair of values: (i) the relative contribution ofthe short-lifetime (Ta) component of decay, and (ii) the relativecontribution of the long-lifetime (m) component. In the case illustratedin FIG. 20A, the lookup table would provide a relative contribution ofthe short-lifetime component of 100% or approximately 100%, and arelative contribution of the long-lifetime component of 0% orapproximately 0%; in the case illustrated in FIG. 20B, the lookup tablewould provide a relative contribution of the short-lifetime component of0% or approximately 0%, and a relative contribution of the long-lifetimecomponent of 100% or approximately 100%; in the case schematicallyillustrated in FIG. 20C, the lookup table would provide a relativecontribution of the short-lifetime component of 50% or approximately50%, and a relative contribution of the long-lifetime component of 50%or approximately 50%. The actual values of each relative contributionare provided in the lookup table previously generated in a calibrationprocess, where reference supercurve area values are obtained from setsof samples with only a short-lifetime component, only a long-lifetimecomponent, and known mixtures of both components in varying ratios, fromratios approaching pure short-lifetime component (such as, e.g.,3,000:1, 10,000:1, or higher) to ratios approaching pure long-lifetimecomponent (such as, e.g., 1:3,000, 1:10,000, or higher), throughintermediate ratios (such as, e.g., 1,000:1, 300:1, 100:1, 30:1, 10:1,3:1, 1:1, 1:3, 1:10, 1:30, 1:100, 1:300, and 1:1,000). A lookup tablemay be provided with relatively few, such as, e.g., 10 or less, valuesof supercurve area; relatively many, such as, e.g., 1,000,000 or more,values of supercurve area; or a relatively intermediate number, such as,e.g., 100, 1,000, 10,000, or 100,000, values of supercurve area. Theprocess of comparing the measured supercurve area with values in thelookup table may comprise finding the closest value in the table,interpolating linearly between the two closest values, interpolatingquadratically between the two closest values, extrapolating beyond thefirst or last value linearly, quadratically, or based on higher-orderpolynomial or on exponential curves, or any number of additionalnumerical estimation processes well known in the art. Once the closestvalue in the lookup table (or an interpolated or extrapolated value) isfound, the corresponding pair of relative contributions (or thecorresponding pair of interpolated or extrapolated relativecontributions) of each lifetime component is obtained. Each relativecontribution is then multiplied by the peak unnormalized intensity valueof the supercurve reached at time 2031 in order to obtain the absoluteintensity contributions of each lifetime component to the measuredsignal (these values are equivalent to ones known in the art as “height”or “peak” signal values). Each relative contribution is optionallymultiplied by a factor S in order to obtain the absolute areacontributions of each lifetime component to the measured signal (thesevalues are equivalent to ones known in the art as “area” signal values):the factor S is calculated by adding the peak values of the raw signalpulses (i.e., pulses 1151, 1152, . . . , 1156 in FIG. 11E) andmultiplying the result by pulse-to-pulse spacing 1166. To increase theaccuracy of computation of the “area” signal values, standard methods ofnumerical integration as are well known in the art may be optionallyemployed in determining factor 5, including, without limitation, thetrapezoidal rule, quadrature, splines, and interpolation. For theavoidance of confusion, the “area” signal values referred to here referto an estimation of the area under the total envelope 1115 ofinteraction between a particle and the light beam in FIG. 11A, whereasthe “area under the supercurve” refers to an estimation of the areaunder supercurve 1165 in FIG. 11F.

While certain embodiments described herein used an exemplaryconfiguration of a multiplexing system with two lifetime bins, it willbe readily apparent to someone of skill in the art that otherconfigurations, including ones with three, four, or more lifetime bins,are encompassed by this disclosure, an example of which is described inconnection with FIGS. 21A-21B. While the illustrative exemplar describedherein is based on representing and operating on supercurves, summedintensity values, and areas on linear scales, alternative embodimentscomprise representing and operating on supercurves, summed intensityvalues, and areas on logarithmic or other scales. It will also bereadily apparent to someone of skill in the art that while a specificexample is given of a method involving a lookup table generated with twospecific lifetime components (τ_(a) and τ_(b)) in varying ratios, asystem of the current disclosure may also be provided with two, three,or more separate lookup tables, each table referring to a combination ofdifferent lifetimes (including, without limitation, for the case of twolifetimes: a common short lifetime τ_(a) and a different long lifetimeτ_(c); a common long lifetime τ_(b) and a different short lifetimeτ_(d); a different short lifetime τ_(e) and a different long lifetimeτ_(f), and additional similar such combinations). The system may beprovided with an option for the user to select a certain combination oflifetimes based on the type of assay being performed, or the system maybe provided with a fixed configuration of certain lifetimes.Furthermore, each detection channel may be provided with the same lookuptable, or each detection channel may be provided with a different lookuptable (or a set of lookup tables) based on the combination of lifetimesassigned by default to, or selectable by the user for, that channel.

FIGS. 22A and 22B illustrate exemplary flow cytometry fluorescencehistograms in populations of eosinophils (indicated as “Eos” in FIGS.22A and 22B), a granulocytic subset of white blood cells known in theart to exhibit relatively high levels of autofluorescence. FIG. 22A, arepresentative diagram illustrating limitations of the prior art, showshistogram distributions of flow cytometrically analyzed stained cellpopulation 2220 (“stained Eos”) and unstained cell population 2210(“unstained Eos”). Both populations are interrogated with laser light at375 nm, and emitted light is collected, spectrally filtered into a bandranging from approximately 442 nm to approximately 478 nm, and detectedby a photodetector. The stained population is labeled with the conjugateCD45/AlexaFluor405; the observed signal comprises both signal from boundCD45/AlexaFluor405 (exogenous fluorescence) and cellularautofluorescence from the eosinophils themselves (endogenousfluorescence). The unstained population is not labeled, and thereforepresents no fluorescence except its own cellular autofluorescence. AsFIG. 22A shows, the two populations overlap nearly completely due to therelatively elevated levels of autofluorescence in the samples. In otherwords, the system of the prior art is unable to effectively discriminateautofluorescence from exogenous fluorescence in the detected signal,drastically reducing assay sensitivity. This makes it difficult toanalyze eosinophil populations in the prior art.

By contrast, FIG. 22B, a representative diagram illustratingimprovements of the present disclosure, shows histogram distributions ofstained cell population 2225 (“stained Eos”) and unstained cellpopulation 2215 (“unstained Eos”) analyzed on one flow cytometricembodiment of the present disclosure. The samples, labeling, excitation,and detection parameters are similar to those in FIG. 22A, except thatin FIG. 22B hardware configuration and lifetime component analysisaccording to the present disclosure were used to discriminate, separate,and subtract a short-lifetime component, attributable to cellularautofluorescence, from a long-lifetime component, attributable toexogenous fluorescence. As a result, the unstained population 2215,thereby removed of interference from autofluorescence, becomes wellseparated from and no longer overlaps with the stained population 2225(likewise removed of interference from autofluorescence), significantlyimproving the ability of the apparatus and methods of the presentdisclosure to analyze cellular populations.

FIGS. 13A-13B illustrate exemplary embodiments of two steps of ananalysis and sorting method of the current disclosure. In FIGS. 13A-13B,the relative orientation of fluid flow, light propagation, andtransverse directions is shown as the set of axes x, z, and y,respectively. The assignment of the axes and directions is similar tothat in FIGS. 9 and 10, however the orientation of the axes with respectto the page is rotated as compared to FIGS. 9 and 10, with the lightpropagation and flow directions being in the plane of the page in FIGS.13A and 13B. Each of the two figures shows a schematic representation ofa side view of the interrogation region 1331 and sorting region 1332 ofthe flowcell 1300. The focusing region of the flowcell, if provided,e.g., by hydrodynamic means, is to the left of the picture; the samplecore stream 1330, surrounded by the sheath fluid 1320, comes in from theleft and flows towards the right. The sheath fluid 1320 is bounded bythe inner walls of the flowcell 1300, and the sample core stream 1330 isbounded by the sheath fluid 1320. In the interrogation region 1331 atleft, one or more beams of pulsed optical energy 1340 are delivered tothe flowcell by relay/focusing optics and intersect the sample corestream 1330. In the sorting region 1332 at right, one or more actuators(shown in the picture as actuator 1380) are provided in contact with ornear the flowcell, positioned in such a way as to overlay the positionof the sample core stream 1330.

FIG. 13A shows a first time step in the processing of a sample whereby asingle particle 1355 in the sample core stream 1330 enters theinterrogation region 1331 (where the beam or beams 1340 intersect thesample core stream 1330). The light-particle interaction generates lightsignals as described above in reference to FIG. 9 or 10, which lightsignals are collected and relayed to one or more detectors. Thedetector(s) record the optical interaction signals generated by theparticle 1355, and convey that information to the signal processing unitas illustrated schematically in FIG. 8. As described above in referenceto FIG. 8, the processing unit uses that information to produce, ifcertain predetermined criteria are met, a triggering signal for anactuator driver, which driver in turn activates the actuator 1380 inFIG. 13A. FIG. 13B shows a second time step in the processing of thesample whereby the particle 1355 detected in the step depicted in FIG.13A, after following path 1365 in the flowcell along direction x,arrives at a point in the vicinity of the actuator 1380 in the sortingregion 1332 of the flowcell. The design of the flowcell, of the opticallayout of the actuator, and of the detection, processing, and controlelectronics is such that the actuator is activated as such a time whenthe passing particle is calculated, estimated, predicted, or found, uponcalibration or determined empirically during instrument design orassembly, to be nearest to a position where activation of the actuatorresults in the desired diversion of the core stream to one of the one ormore sorting channels. The timing of the triggering signal (i.e., therelative delay from particle detection to sorting actuation) is designedto take into account both the average velocity of fluid flow in theflowcell and its spatial profile across the flowcell cross-section,according to the characteristics of Poiseuille flow known in the art andas modified based on empirical or modeling information.

In FIGS. 14A and B, 15A and B, 16A and B, and 17A-17D, the relativeorientation of fluid flow, light propagation, and transverse directionsis shown as the set of axes x, z, and y, respectively. The assignment ofthe axes and directions is similar to that in FIGS. 9 and 10, howeverthe orientation of the axes with respect to the page is rotated ascompared to FIGS. 9 and 10, with the fluid flow and transversedirections being in the plane of the page in FIGS. 14A and B, 15A and B,16A and B, and 17A-17D. The cross-sectional plane depicted in FIGS. 14Aand B, 15A and B, 16A and B, and 17A-17D is the plane that contains thesample core stream.

FIGS. 14A and 14B illustrate one embodiment of two states of theparticle analysis and sorting method of the current disclosure. Each ofthe two figures shows a schematic representation of a cross-sectionalview of the sorting region of the flowcell. Similarly to the situationdepicted in FIGS. 13A and 13B, the focusing region of the flowcell,e.g., by hydrodynamic means, if provided, is to the left of the picture;the sample core stream 1430, surrounded by the sheath fluid 1420, comesin from the left and flows towards the right. The sheath fluid 1420 isbounded by the inner walls of the flowcell 1400, and the sample corestream 1430 is bounded by the sheath fluid 1420. The flowcell 1400splits into two channels in the sorting region: the default (or waste)sorting channel 1446 and the sorting channel 1448. Actuator 1480 isdepicted as embodied in, in contact with, or in proximity of the innerwall of the flowcell 1400 on the default sorting channel side. FIG. 14Ashows the configuration of the default state, where with the actuator1480 in the OFF state, a non-selected particle 1450 in the sample corestream 1430 flows by design into the default sorting channel 1446.Similarly to the state depicted in FIG. 13B, FIG. 14B shows theconfiguration of the sorting state, where with the actuator 1480 in theON state, a transient gas, vapor, or gas-vapor bubble, or a region ofheated or cooled, less-dense sheath fluid 1495 is generated (by meansincluding, without limitation, thermal means, electrolytic means, and/orgas injection means), which creates a localized flow diversion in thedepicted cross-sectional plane and in its vicinity, which diversiontemporarily deflects the sample core stream 1431 into the sortingchannel 1448, which sample core stream contains a particle 1455 detectedupstream and automatically selected by analysis circuitry and/oralgorithms to trigger sorting actuation. Following deactivation of theactuator 1480, the transient gas, vapor, gas-vapor bubble or region ofless-dense fluid 1495 shrinks or is cleared away, and the flow patternreturns to the original default state of FIG. 14A.

FIGS. 15A and 15B illustrate another embodiment of two states of aparticle analysis and sorting method of the current disclosure. It issimilar to the embodiment illustrated in FIGS. 14A and 14B, except inthe design and nature of actuation. Here the actuator 1580 is located inproximity to an expandable chamber 1597 adjacent to the flowcell innerwall and separated from the sheath fluid 1520 by a flexible membrane1596. With the actuator 1580 in the OFF or default state as shown inFIG. 15A, the expandable chamber 1597 is in its default configuration ata pressure designed to match the pressure of the fluid inside theflowcell at the location of the membrane, resulting in a flat shape ofthe membrane to match the shape of the flowcell inner wall, and anon-selected particle 1550 in the sample core stream 1530 flows bydesign into the default (or waste) sorting channel 1546. With theactuator 1580 in the ON or sorting state as shown in FIG. 15B, theexpandable chamber 1597 is pressurized (by means including, withoutlimitation, thermal means, mechanical means, hydraulic and/or gasinjection means) to a higher pressure than in the default configuration;this pressure differential causes the membrane 1596 to flex into theflowcell until a new equilibrium is reached. The bulging membrane causesthe flow pattern to shift in a similar way to that previously shown forFIG. 14B, resulting in the sample core stream 1531 being temporarilydiverted into the sorting channel 1548, which sample stream contains aparticle 1555 detected upstream and automatically selected by analysiscircuitry and/or algorithms to trigger sorting actuation. Followingdeactivation of the actuator 1580, the expandable chamber 1597 isallowed to or made to return to its default pressure state, the membrane1596 returns to its default flat shape, and the flow pattern returns tothe original default configuration of FIG. 15A.

FIGS. 16A and 16B illustrate yet another embodiment of two states of aparticle analysis and sorting method of the current disclosure. It issimilar to the embodiment illustrated in FIGS. 15A and 15B, except inthe design of actuation. Sorting actuation here is realized by means oftwo actuators, positioned on opposite sides of the flowcell, eachactuator being located in proximity to expandable/compressible chambers[1697 for the default (or waste) side and 1699 for the sort side]adjacent to the flowcell inner wall and separated from the sheath fluid1620 by a flexible membrane (1696 for the default side and 1698 for thesort side). In the default state, depicted in FIG. 16A, the expandablechambers 1697 and 1699 of both the default-side and sort-side actuatorsare in their default configuration at pressures designed to match thepressure of the fluid inside the flowcell at the location of themembranes 1696 and 1698, resulting in flat shapes of the membranes tomatch the shape of the flowcell inner walls. In this non-sorting state,a non-selected particle 1650 in the sample core stream 1630 flows bydesign into the default (or waste) sorting channel 1646. In the sortingstate, depicted in FIG. 16B, the expandable chamber 1697 of thedefault-side actuator 1680 is pressurized (by means including, withoutlimitation, heating means, mechanical means, hydraulic means, and/or gasinjection means), through actuation, in a similar way as depicted inreference to FIG. 15B; this pressure differential with respect to thelocal pressure in the sheath fluid causes the membrane 1696 to bulgeinto the flowcell until a new equilibrium is reached. Simultaneously,the compressible chamber 1699 of the sorting side actuator 1681 isdepressurized (by means including, without limitation, cooling means,mechanical means, hydraulic means, and/or gas aspiration means), throughactivation of actuator 1681, to a lower pressure than in the defaultconfiguration; this pressure differential with respect to the localpressure in the sheath fluid causes the membrane 1698 to flex away fromthe flowcell until a new equilibrium is reached. The combination of theinwardly bulging default-side membrane 1696 and the outwardly flexingsort-side membrane 1698 causes the flow pattern to shift in a similarway to that previously shown for FIGS. 14B and 15B, resulting in thesample core stream 1631 being temporarily diverted into the sortingchannel 1648, which sample stream contains a particle 1655 detectedupstream and automatically selected by analysis circuitry and/oralgorithms to trigger sorting actuation. Following deactivation of theactuator pair, both the default-side and the sort-sideexpandable/compressible chambers 1697 and 1699 are allowed to or made toreturn to their default pressure states, both the default-side and thesort-side membranes 1696 and 1698 return to their default flat shapes,and the flow pattern returns to the original default configuration ofFIG. 16A.

FIGS. 17A-17D illustrate a multi-way sorting embodiment of a particleanalysis and sorting method of the current disclosure. Each of the fourfigures shows a schematic representation of a cross-sectional view ofthe sorting region of the flowcell. The configuration is similar to thatdepicted in reference to FIGS. 14A and 14B, except that instead of asingle sorting channel, a plurality of sorting channels 1741-1744 isprovided along a transverse direction y. One advantage of thisembodiment is the ability to have a plurality of different receptaclesinto which the sample may be sorted, depending on the result of theupstream analysis by the interrogating light beam, the signal detectors,and associated electronic and logic trigger circuitry and/or algorithms.For example, the signals detected in response to the upstreaminterrogation of the sample may indicate that a particle, e.g., particle1751, was detected with a certain set A of properties targeted forselection (e.g., the presence of surface antigens or intracellularmarkers associated with certain kinds of cancer cells). It may bedesirable to sort particles having these properties into a certaincollection receptacle, e.g., one provided to receive the outflow fromsorting channel 1741, as illustrated in FIG. 17B. Another particle,e.g., particle 1752, may flow through the interrogation point andproduce signals that indicate the presence of a different set B ofproperties targeted for selection (e.g., the presence of surfaceantigens or intracellular markers associated with certain kinds of stemcells). It would be desirable to sort particles like particle 1752having set-B properties into a different receptacle from that designedfor collection of particles having set-A properties: e.g., a receptacleprovided to receive the outflow from sorting channel 1742, asillustrated in FIG. 17C. Likewise for yet another set D of properties,particles like particle 1754 detected as having those properties, and asorting channel 1744 designed to flow into a receptacle to collect suchparticles. Accordingly, the embodiment illustrated in FIGS. 17A-17Dprovides an example of such a multi-way sorting capability of thecurrent disclosure, with a number of sorting channels 1741-1744 inaddition to the default (or waste) sorting channel 1746. FIGS. 17A-17Dexemplarily show four such sorting channels explicitly. It will beapparent to those skilled in the art that additional configurationshaving more or less than four sorting channels, in addition to thedefault sorting channel, do not depart from the scope of the currentdisclosure.

Each of the sorting channels 1741-1744 (as well as the default sortingchannel 1746) may optionally be connected with a receiving receptacledesigned to collect the fluid flow from the respective channel. Theselection of a particular sorting channel (or of the default sortingchannel) as the target for reception of a desired sorted portion of thesample core stream is effected by actuation of actuator 1780. In atwo-way sort there are two principal sorting states, which can bedescribed as OFF (default) and ON (sorting) as described above inrelation to FIGS. 14A-14B, 15A-15B, and 16A-16B. In a multi-way sort, onthe other hand, there generally can be as many sorting states as thereare sorting “ways” or possible sorting channels. With reference to FIGS.17A-17D, five possible sorting channels are indicated (the defaultsorting channel 1746 plus four sorting channels 1741-1744); accordingly,this is referred to as a five-way sort (or, alternatively, as a four-waysort, the default/waste sorting channel being excluded from the count insuch case). An actuation process is provided to result in differentdegrees of deflection of the sample core stream portion, correspondingto the selection of different intended sorting channels.

In FIG. 17A actuator 1780 is depicted as embodied in, in contact with,or in proximity of the inner wall of the flowcell 1700 on the defaultsorting channel side. Similarly to the state depicted in FIG. 14A, FIG.17A shows the configuration of the default state, where with theactuator 1780 in the OFF state, a non-selected particle 1750 in thesample core stream 1730 flows by design into the default sorting channel1746. Similarly to the state depicted in FIG. 14B, FIGS. 17B-17D showthe configurations of various sorting states, where with the actuator1780 in the ON state at levels 1, 2, and 4, respectively, transientregions 1791, 1792, and 1794, respectively (comprising, withoutlimitation, a gas, vapor, gas-vapor bubble, or a less-dense region ofsheath fluid), are generated (by means including, without limitation,thermal means, electrolytic means, and gas injection means), whichcreate respective localized flow diversions in the depictedcross-sectional plane and in its vicinity, which diversions temporarilydeflect the sample core stream into configurations 1731, 1732, and 1734,respectively, and cause the corresponding particles 1751, 1752, and1754, respectively, to flow into the respective sorting channels 1741,1742, and 1744. Following deactivation of the actuator, the transientgas bubble shrinks or is cleared away, and the flow pattern returns tothe original default state of FIG. 17A. Not shown is the configurationof a sorting state intermediate to the sorting states of FIGS. 17C and17D, corresponding to an actuation level 3, whereby a transient regionof a size intermediate between that of regions 1792 and 1794 temporarilydiverts the sample core stream into sorting channel 1743.

Throughout this disclosure the term “default sorting channel” (or “wastesorting channel”) is associated with an OFF state of an actuator,signifying a passive state in which no particle sorting is performed,and in which the sample core stream and particles therein are typicallyoutflowed and discarded as undesired waste. The term “sorting channel”is associated with an ON state of an actuator, signifying an activatedstate of an actuator, in which active sorting of a desired particle isperformed. While for some embodiments this may be a preferredconfiguration, the current disclosure is not so limited, and includedunder the scope of the disclosure are embodiments where a passive stateof an actuator is associated with collection of desired particles, andan active state of an actuator is associated with generation of a wastestream of undesired particles from the particle analyzer/sorter.

Referring to FIG. 18B, a flow chart is provided that describes asequence of principal steps involved in the performance of a method ofparticle analysis in accordance with an embodiment of the presentdisclosure. A first step 1840 involves the generation of one or moretrains of optical pulses (or other modulated output of light from one ormore sources) for optical interrogation of particles in a sample. Asecond step 1842 involves the presentation of a sample, or theintroduction of a sample, to the apparatus by a user or operator. Athird, optional, step 1844 involves the mixing, reaction, and incubationof the sample with one or more reagents, which reagents may be preloadedonboard the apparatus or may be introduced by the user or operator. Afourth step 1846 involves the formation, by means of, e.g., hydrodynamicfocusing of the sample by sheath fluid, of a core stream of particlesflowing essentially in single file in the microchannel portion of theflowcell for optical interrogation. A fifth step 1848 involves theinterrogation, by optical interaction, of a single particle in thesample core stream by the one or more trains of optical pulses,resulting in the generation of optical interaction signals. A sixth step1850 involves the collection of the optical interaction signals, theoptical filtering of the collected optical signals, and the spectralisolation of the filtered optical signals. A seventh step 1852 involvesthe detection of the spectrally isolated optical signals, thetransduction of said signals into analog electrical signals, thedigitization of the analog electrical signals into digital signals (alsoreferred to as digitizations, digitization points, digital samplings, orsampling points), and the processing of the digital signals. An eighthstep 1854 involves the further application of digital signal processingalgorithms to the digital signals corresponding to each isolatedspectral band so as to isolate the separate contributions of one or morefluorescence lifetime components to each signal. A ninth optional step1856 involves the coherent summing, or coherent averaging, of lifetimesignals coming from different pulses but all originating from the sameparticle under interrogation. A tenth step 1858 involves the recordingand storage of the detected and processed signal parameters, including,without limitation, fluorescence intensity in one or more spectralbands, one or more fluorescence lifetime values in each of the one ormore spectral bands, phase shift, scattering intensity, and absorption.An eleventh step 1860 involves a decision, which may be automated or maybe presented by the system, through a processing unit, to the user oroperator as a call for action, on whether to analyze additionalparticles; if the choice is positive, the method workflow returns to thefifth step above; if the choice is negative, the method workflowcontinues to the next (twelfth) step below. A twelfth optional step 1862involves the classification of a portion or a totality of the eventsdetected and analyzed according to certain criteria (which may include,without limitation, entities commonly referred to in the art as“triggers,” “thresholds,” and “gates”), which may be predetermined andpreloaded into the apparatus or may be selected or modified or createdby the user. A thirteenth step 1864 involves the presentation to theuser or operator of the processed data (which may include, withoutlimitation, the raw detected time-varying signals, a list of detectedparticle-interrogation events, and graphs or plots of detected eventsdisplayed according to characteristics such as, e.g., fluorescencelifetime, fluorescence intensity, absorption, and scattering) by meansof a user interface such as, e.g., a screen, a computer monitor, aprintout, or other such means.

FIG. 19 shows a block diagram of an exemplary embodiment of a dataprocessing system 1900 to provide a particle analysis and sorting systemas described herein. In an embodiment, data processing system 1900 is apart of the control system to perform a method that includes providinglight pulses; providing a sample for analysis; exposing the sample tothe light pulses; detecting optical decay curves; and extracting timeconstants from the optical decay curves, as described herein. In someembodiments, data processing system 1900 is represented by any one ofsignal processing units 790, 890, 990 and 1090 depicted in FIGS. 7, 8,9, and 10, respectively, and further optionally incorporates any one ofdata storage units 792 and 892 depicted in FIGS. 7 and 8, respectively.

Data processing system 1900 includes a processing unit 1901 that mayinclude a microprocessor or microcontroller, such as Intelmicroprocessor (e.g., Core i7, Core 2 Duo, Core 2 Quad, Atom), SunMicrosystems microprocessor (e.g., SPARC), IBM microprocessor (e.g., IBM750), Motorola microprocessor (e.g., Motorola 68000), Advanced MicroDevices (“AMD”) microprocessor, Texas Instrument microcontroller, andany other microprocessor or microcontroller.

Processing unit 1901 may include a personal computer (PC), such as aMacintosh® (from Apple Inc. of Cupertino, Calif.), Windows®-based PC(from Microsoft Corporation of Redmond, Wash.), or one of a wide varietyof hardware platforms that run the UNIX operating system or otheroperating systems. For at least some embodiments, processing unit 1901includes a general purpose or specific purpose data processing systembased on Intel, AMD, Motorola, IBM, Sun Microsystems, IBM processorfamilies, or any other processor families. As shown in FIG. 19, a memory1903 is coupled to the processing unit 1901 by a bus 1923. Memory 1903has instructions and data 1904 stored thereon which when accessed byprocessing unit 1901 cause the processing unit 1901 to perform methodsto provide highly multiplexed particle analysis or lifetime analysis,and optionally sorting, as described herein. Memory 1903 can be dynamicrandom access memory (“DRAM”) and can also include static random accessmemory (“SRAM”). A bus 1923 couples processing unit 1901 to memory 1903and also to a non-volatile storage 1909 and to a display controller 1905(if a display is used) and to input/output (I/O) controller(s) 1911.Display controller 1905 controls in the conventional manner a display ona display device 1907 which can be a cathode ray tube (CRT), a liquidcrystal display (LCD), a light-emitting diode (LED) monitor, a plasmamonitor, or any other display device. Input/output devices 1917 caninclude a keyboard, disk drives, printers, a scanner, a camera, andother input and output devices, including a mouse or other pointingdevice. I/O controller 1911 is coupled to one or more audio inputdevices 1913 such as, for example, one or more microphones.

Display controller 1905 and I/O controller 1911 can be implemented withconventional well-known technology. An audio output 1915 such as, forexample, one or more speakers, may be coupled to I/O controller 1911.Non-volatile storage 1909 is often a magnetic hard disk, an opticaldisk, or another form of storage for large amounts of data. Some of thisdata is often written, by a direct memory access process, into memory1903 during execution of software in data processing system 1900 toperform methods described herein.

One skilled in the art will immediately recognize that the terms“computer-readable medium” and “machine-readable medium” include anytype of storage device that is accessible by processing unit 1901. Dataprocessing system 1900 can interface to external systems through a modemor network interface 1921. It will be appreciated that modem or networkinterface 1921 can be considered to be part of data processing system1900. This interface 1921 can be an analog modem, ISDN modem, cablemodem, token ring interface, satellite transmission interface, Wi-Fi,Bluetooth, cellular network communication interface, or other interfacesfor coupling a data processing system to other data processing systems.

It will be appreciated that data processing system 1900 is one exampleof many possible data processing systems which have differentarchitectures. For example, personal computers based on an Intelmicroprocessor often have multiple buses, one of which can be aninput/output (I/O) bus for the peripherals and one that directlyconnects processing unit 1901 and memory 1903 (often referred to as amemory bus). The buses are connected together through bridge componentsthat perform any appropriate translation due to differing bus protocols.

Network computers are another type of data processing system that can beused with the embodiments as described herein. Network computers do notusually include a hard disk or other mass storage, and the executableprograms are loaded from a network connection into memory 1903 forexecution by processing unit 1901. A typical data processing system willusually include at least a processor, memory, and a bus coupling thememory to the processor.

It will also be appreciated that data processing system 1900 can becontrolled by operating system software which includes a file managementsystem, such as a disk operating system, which is part of the operatingsystem software. Operating system software can be the family ofoperating systems known as Macintosh® Operating System (Mac OS®) or MacOS X® from Apple Inc. of Cupertino, Calif., or the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. The file management systemis typically stored in non-volatile storage 1909 and causes processingunit 1901 to execute the various acts required by the operating systemto input and output data and to store data in memory, including storingfiles on non-volatile storage 1909.

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement methods described herein. Anon-transitory machine-readable medium can be used to store software anddata which when executed by a data processing system causes the systemto perform various methods described herein. This executable softwareand data may be stored in various places including for example ROM,volatile RAM, non-volatile memory, and/or cache. Portions of thissoftware and/or data may be stored in any one of these storage devices.

Thus, a machine-readable medium includes any mechanism that provides(i.e., stores and/or transmits) information in a form accessible by amachine (e.g., a computer, network device, or any device with a set ofone or more processors, etc.). For example, a machine-readable mediumincludes recordable/non-recordable media [e.g., read only memory (ROM);random access memory (RAM); magnetic disk storage media; optical storagemedia; flash memory devices; and the like].

It will be further appreciated that data processing system 1900 may befunctionally implemented by allocating several of its functions todistributed units or modules separate from a central system. In someembodiments, some or all of the signal processing functions as depicted,e.g., in FIGS. 7-10, illustrated in FIGS. 11A-11F and 12A-12B, anddescribed in FIGS. 18A-18C, may be performed by signal processing unitsor modules physically separate from data processing system 1900, yetconnected with it for performance of other functions, such as, e.g.,input/output, display, data storage, memory usage, bus usage, additionalsignal processing functions, and both specific-purpose andgeneral-purpose data processing functions. In some embodiments, some orall of the data storage functions as depicted, e.g., in FIGS. 7-8,illustrated in FIGS. 11A-11F and 12A-12B, and described in FIGS.18A-18C, may be performed by data storage units or modules physicallyseparate from data processing system 1900, yet connected with it asdescribed above. In some embodiments, some or all of the signalprocessing functions mentioned may be performed by processing unit 1901internal to data processing system 1900, and in some embodiments some orall of the data storage functions mentioned may be performed bynon-volatile storage unit 1909 and/or memory unit 1903 internal to dataprocessing system 1900.

The methods as described herein can be implemented using dedicatedhardware (e.g., using Field Programmable Gate Arrays, Digital SignalProcessing chips, or Application Specific Integrated Circuits) or sharedcircuitry (e.g., microprocessors, microcontrollers, single-boardcomputers, standalone computers, or cloud-based processors on remoteservers) under control of program instructions stored in amachine-readable medium. The methods as described herein can also beimplemented as computer instructions for execution on a data processingsystem, such as system 1900 of FIG. 19.

It will be appreciated by those skilled in the art that aspects of thepresent disclosure, while illustrated with reference to applications toparticle analysis and sorting and particularly to flow cytometry, alsopresent advantages in other application areas. The concept of lifetimebinning as a means to simplify the performance of lifetime measurementsand thereby enable higher degree of multiplexing than hithertopractical, for example, is also advantageous to the field of imaging, inparticular to the field of microscopy, and more particularly to thefield of fluorescence microscopy, including confocal scanningmicroscopy. Whereas in a flow cytometer an “event” is defined as thepassage of a particle through the interrogation area, in microscopy theroughly equivalent element is a “pixel” (or “voxel” in certainapplications of confocal scanning microscopy), defined as the smallestresolvable unit of an image (or of a three-dimensional measured volumein certain applications of confocal scanning microscopy). Spectralspillover and crosstalk is a problem in fluorescence microscopy just asit is a problem in flow cytometry, and the present disclosure offers asolution to both by providing a greater degree of multiplexing, areduced level of spectral spillover, a reduced interference fromautofluorescence, or a combination of the three. The present disclosureadmits of implementation within the framework of a fluorescencemicroscope in ways that parallel very closely the specific examplesgiven in the case of flow-based particle analysis. A microscopyapplication of the present disclosure, for example, would rely on asystem configuration very similar to that of FIG. 7, with the fluidelements 740, 742, 700, and 740 replaced by a suitable sample holder,such as are well known in the art, for exposure of the sample to thebeam; and similarly for FIGS. 9 and 10. In other words, adaptation ofthe present disclosure to the field of microscopy is fully within thescope of this disclosure, given the descriptions of the novel apparatusand methods herein. One specific application of fluorescence microscopythat would benefit from the present disclosure is in vivo imaging, suchas, e.g., methods and apparatuses used for medical diagnostics. Theseinclude the analysis and diagnosis of externally optically accessibleorgans, such as the skin and the eye, as well as organs opticallyaccessible through the use of endoscopes, such as the respiratory tract,the gastrointestinal tract, and, in the context of surgery, any otherorgan or part of the body. As in the case of laboratory-basedfluorescence microscopy, adaptation of the apparatuses and methodsdescribed herein is entirely within the scope of the present disclosure,requiring only minor modifications of the apparatus and process stepsfrom the illustrative examples that are provided. The usefulness of thepresent disclosure is therefore not to be circumscribed to the examplesand figures provided, but extends to the full scope of what is claimed.

It will be further appreciated by those skilled in the art that theconcept of lifetime binning as a means to enable higher degree ofmultiplexing than hitherto possible or practical, for example, is alsoadvantageous to the field of bead-based multiplexing assays. The variousmethods and systems described herein can be applied to the task ofbead-based multiplexing with only minor modifications, such asaccounting for the predesigned levels of each color-coding dye presentin a bead in order to identify which combination of dye level(s) aparticular bead belongs to. In an exemplary embodiment, microspheres(of, e.g., polystyrene or other materials) are formed so as toincorporate a dye having a relatively short fluorescence lifetime (e.g.,dye A, having a lifetime shorter than, e.g., about 5 ns) and another dyehaving a relatively long fluorescence lifetime (e.g., dye B, having alifetime longer than, e.g., about 10 ns), both such dyes havingsubstantially overlapping spectra so as to permit their detection by thesame optical apparatus. Each microsphere is prepared with one of a setof discrete amounts of each dye, e.g., an amount substantially orapproximately equal to 1000, 1500, 2250, 3375, 5063, 7594, 11391, 17086,25629, or 38443 molecules. In certain embodiments, more or less than theten discrete amounts described in this exemplary embodiment may bepreferable. In certain embodiments, the ratio of one amount to theimmediately lower one may be preferably more or less than the ratio of1.5 described in this exemplary embodiment. In certain embodiments, theratios between amounts may preferably be other than the uniform ratiodescribed in this exemplary embodiment. In certain embodiments, thelowest amount may preferably be more or less than the 1000 moleculesdescribed in this exemplary embodiment. The various combinations of thetwo dyes in different amounts comprise a multiplexing set; in thisexemplary embodiment, the use of ten different amounts for each of twodyes yields 100 different combinations, each combination correspondingto a different type of coded microsphere. Each type of coded microspherein turn is used for a different capture assay: e.g., beads having thecombination of 1000 molecules of dye A and 1000 molecules of dye B areprepared with, e.g., capture antibodies specific to antigen X; beadshaving the combination of 1500 molecules of dye A and 1000 molecules ofdye B are prepared with, e.g., capture antibodies specific to antigen Y;and so forth. The beads so prepared are then used in, e.g., multiplexingsandwich immunoassays, multiplexing nucleic-acid assays, or othermultiplexing assays as described herein and according to methods wellknown to those skilled in the art.

An exemplary embodiment of an instrument used for bead-basedmultiplexing assays according to the present disclosure comprises: apulsed light source designed to simultaneously excite dye A and dye B ineach bead in a sample of beads suspended in buffer medium as the beadflows in single file through a flowcell; an optical apparatus designedto collect fluorescence emission decays from dye A and dye B; and asignal processing apparatus designed to extract the contributions fromdye A and dye B to the observed decay signals; as well as a light sourcedesigned to excite one or more type of fluorescent reporter molecules;an optical apparatus designed to collect fluorescence emission from thereporter molecule(s) present on each detected bead; and a signalprocessing apparatus designed to measure the reporter fluorescenceemission or emissions from each detected bead. The apparatus in thisexemplary embodiment uses the detected levels of fluorescence in thebead-coding channel(s) to identify the specific dye combination that adetected bead belongs to, and therefore to determine what correspondingassay was performed on that bead. By allowing the simultaneousidentification of each possible combination of dye amounts, and byincorporating fluorescence lifetime as an additional parameter fordiscrimination of bead-coding dyes, the apparatus and method of thepresent disclosure allows the multiplexing of assays in greater numbersthan hitherto possible or practical. In certain embodiments, more thantwo dyes having overlapping spectra and having distinct lifetimes areused to code microspheres. In certain embodiments, more than onespectral channel of detection is allocated for the purpose of codingmicrospheres, each channel allowing the use of one or more dyes withoverlapping spectra and distinct lifetimes. In certain embodiments, morethan one light source is allocated for the purpose of exciting dyes usedto code microspheres, and more than one optical apparatus is used tocollect the light emitted by microsphere-coding dyes.

A method of analyzing particles in a sample using a particle analyzer isdisclosed, comprising the steps of:

-   -   providing a source of a beam of pulsed optical energy;    -   providing a sample holder configured to expose a sample to the        beam;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect optical signals resulting from        interactions between the beam and the sample, the channels being        further configured to convert the optical signals into        respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a signal processing module;    -   exposing the sample to the beam, and    -   using the signal processing module for:    -   receiving the electrical signals from the detector, wherein the        electrical signals represent a time-domain sequence of pulse        signals;    -   segmenting the sequence into equal pulse signal segments each        comprising substantially a same number of digitization elements,        wherein a length of each segment corresponds to an excitation        pulse repetition period;    -   coherently adding the individual pulse signal segments, based on        individual digitization elements, to form a supercurve, wherein        each digitization index of the supercurve corresponds to a value        equal to a sum of all the signal values from the corresponding        digitization indices from all pulse signal segments, and the        supercurve comprises at least two lifetime components, each        lifetime component having a slope that is inversely proportional        to a value of a corresponding time constant; and    -   quantifying an intensity contribution of a first short-lifetime        component and a second long-lifetime component relative to an        overall intensity of the supercurve.

A method of analyzing and sorting particles in a sample using a particleanalyzer/sorter is disclosed, comprising the steps of:

-   -   providing an internally modulated laser as a source of a beam of        pulsed optical energy;    -   providing a flowcell configured as an optical excitation chamber        for exposing to the beam a sample comprising a suspension of        particles and for generating optical signals from interactions        between the beam and the particles;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect fluorescence optical signals        resulting from interactions between the beam and the particles        in the sample, the channels being further configured to convert        the optical signals into respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a flow path for the suspension of particles;    -   providing connections between the flowcell and each of the flow        path, the first optical path, and the second optical path;    -   providing a signal processing module comprising one of an FPGA,        a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller,        a single-board computer, a standalone computer, and a        cloud-based processor;    -   exposing the particles in the sample to the beam;    -   using the signal processing module for:    -   receiving the electrical signals from the detector, wherein the        electrical signals represent a time-domain sequence of pulse        signals;    -   segmenting the sequence into equal pulse signal segments each        comprising substantially a same number of digitization elements,        wherein a length of each segment corresponds to an excitation        pulse repetition period;    -   coherently adding the individual pulse signal segments, based on        individual digitization elements, to form a supercurve, wherein        each digitization index of the supercurve corresponds to a value        equal to a sum of all the signal values from the corresponding        digitization indices from all pulse signal segments, and the        supercurve comprises at least two lifetime components, each        lifetime component having a slope that is inversely proportional        to a value of a corresponding time constant;    -   quantifying an intensity contribution of a first short-lifetime        component and a second long-lifetime component relative to an        overall intensity of the supercurve;    -   providing a particle sorting actuator connected with the flow        path, based on at least one flow diversion in the flow path, and        further based on one of a transient bubble, a pressurizable        chamber, a pressurizable/depressurizable chamber pair, and a        pressure transducer;    -   providing an actuator driver connected with the actuator, the        driver being configured to receive actuation signals from the        signal processing module;    -   providing at least one particle collection receptacle; and    -   collecting at least one particle from the suspension of        particles in the particle collection receptacle.

A method of analyzing particles in a sample using a particle analyzer isdisclosed, comprising the steps of:

-   -   providing a source of a beam of pulsed optical energy;    -   providing a sample holder configured to expose a sample to the        beam;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect optical signals resulting from        interactions between the beam and the sample, the channels being        further configured to convert the optical signals into        respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a signal processing module;    -   exposing the sample to the beam, and    -   using the signal processing module for:    -   receiving the electrical signals from the detector, wherein the        electrical signals represent a time-domain sequence of pulse        signals;    -   segmenting the sequence into equal pulse signal segments each        comprising substantially a same number of digitization elements,        wherein a length of each segment corresponds to an excitation        pulse repetition period;    -   coherently adding the individual pulse signal segments, based on        individual digitization elements, to form a supercurve, wherein        each digitization index of the supercurve corresponds to a value        equal to a sum of all the signal values from the corresponding        digitization indices from all pulse signal segments, and the        supercurve comprises at least two lifetime components, each        lifetime component having a slope that is inversely proportional        to a value of a corresponding time constant;    -   allocating each lifetime component of the supercurve to discrete        bins of predetermined time constants; and    -   quantifying an intensity contribution of a first short-lifetime        component and a second long-lifetime component relative to an        overall intensity of the supercurve based on the allocation of        the individual lifetime components to the discrete bins.

A method of analyzing and sorting particles in a sample using a particleanalyzer/sorter is disclosed, comprising the steps of:

-   -   providing an internally modulated laser as a source of a beam of        pulsed optical energy;    -   providing a flowcell configured as an optical excitation chamber        for exposing to the beam a sample comprising a suspension of        particles and for generating optical signals from interactions        between the beam and the particles;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect fluorescence optical signals        resulting from interactions between the beam and the particles        in the sample, the channels being further configured to convert        the optical signals into respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a flow path for the suspension of particles;    -   providing connections between the flowcell and each of the flow        path, the first optical path, and the second optical path;    -   providing a signal processing module comprising one of an FPGA,        a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller,        a single-board computer, a standalone computer, and a        cloud-based processor;    -   exposing the particles in the sample to the beam;    -   using the signal processing module for:    -   receiving the electrical signals from the detector, wherein the        electrical signals represent a time-domain sequence of pulse        signals;    -   segmenting the sequence into equal pulse signal segments each        comprising substantially a same number of digitization elements,        wherein a length of each segment corresponds to an excitation        pulse repetition period;    -   coherently adding the individual pulse signal segments, based on        individual digitization elements, to form a supercurve, wherein        each digitization index of the supercurve corresponds to a value        equal to a sum of all the signal values from the corresponding        digitization indices from all pulse signal segments, and the        supercurve comprises at least two lifetime components, each        lifetime component having a slope that is inversely proportional        to a value of a corresponding time constant;    -   allocating each lifetime component of the supercurve to discrete        bins of predetermined time constants;    -   quantifying an intensity contribution of a first short-lifetime        component and a second long-lifetime component relative to an        overall intensity of the supercurve based on the allocation of        the individual lifetime components to the discrete bins;    -   providing a particle sorting actuator connected with the flow        path, based on at least one flow diversion in the flow path, and        further based on one of a transient bubble, a pressurizable        chamber, a pressurizable/depressurizable chamber pair, and a        pressure transducer;    -   providing an actuator driver connected with the actuator, the        driver being configured to receive actuation signals from the        signal processing module;    -   providing at least one particle collection receptacle; and    -   collecting at least one particle from the suspension of        particles in the particle collection receptacle.

In the foregoing specification, embodiments of the current disclosurehave been described with reference to specific exemplary embodimentsthereof. It will, however, be evident that various modifications andchanges may be made thereto without departing from the broader spiritand scope of the disclosure. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. An apparatus for analyzing an optical signal decay, comprising: asource of a beam of pulsed optical energy configured to expose a sampleto the beam; a detector comprising a number of spectral detectionchannels sensitive to distinct wavelength sections of theelectromagnetic spectrum, wherein the channels are configured to detectoptical signals resulting from interactions between the beam and thesample and convert the optical signals into respective electricalsignals; a first optical path from the source of the beam to the sample;a second optical path from the sample to the detector; and a signalprocessing module configured to execute a lifetime analysis algorithmcomprising: receiving the electrical signals from the detector, whereinthe electrical signals represent a time-domain sequence of pulsesignals; segmenting the sequence into equal pulse signal segments eachcomprising substantially a same number of sampling points, wherein eachsampling point of each pulse signal segment corresponds to a respectivesampling index, and wherein a length of each pulse signal segmentcorresponds to an excitation pulse repetition period; coherently addinga value of a sampling point corresponding to the respective samplingindex of each pulse signal segment to form a supercurve, wherein eachsampling index of the supercurve corresponds to a value equal to a sumof substantially all the sampling point values from the correspondingsampling indices from each pulse signal segment, and wherein thesupercurve comprises an intensity of at least one lifetime componentover time; determining whether the at least one lifetime component ofthe supercurve comprises one or both of: a short-lifetime component anda long-lifetime component; and quantifying an intensity contribution ofthe at least one lifetime component of the supercurve.
 2. The apparatusof claim 1, wherein the optical signals comprise a fluorescence signal.3. The apparatus of claim 2, wherein, when present, the short-lifetimecomponent comprises autofluorescence and the long-lifetime componentcomprises exogenous fluorescence.
 4. The apparatus of claim 1, whereinthe at least one lifetime component comprises an instrument responsefunction when the short-lifetime component and the long-lifetimecomponent are not present.
 5. The apparatus of claim 1, wherein thesample comprises a suspension of particles, such that the apparatusfurther comprises: a flow path for the suspension of particles; and aflowcell configured as an optical excitation chamber for generating theoptical signals from interactions between the beam of pulsed opticalenergy and the particles, wherein the flowcell is connected with theflow path, the first optical path, and the second optical path.
 6. Theapparatus of claim 5, further comprising: a particle sorting actuatorconnected with the flow path; an actuator driver connected with theactuator, wherein the driver is configured to receive actuation signalsfrom the signal processing module; and at least one particle collectionreceptacle connected with the flow path.
 7. The apparatus of claim 6,wherein the particle sorting actuator is based on at least one flowdiversion in the flow path.
 8. The apparatus of claim 7, wherein theparticle sorting actuator is based on one of: a transient bubble, apressurizable chamber, a pressurizable/depressurizable chamber pair, ora pressure transducer.
 9. The apparatus of claim 1, wherein the signalprocessing module is further configured to extract time constants fromthe supercurve.
 10. The apparatus of claim 9, wherein the signalprocessing module is further configured to allocate each lifetimecomponent of the supercurve to predetermined bins, each bin representinga group of relatively similar lifetime components.
 11. An apparatus foranalyzing an optical signal decay, comprising: a source of a beam ofpulsed optical energy configured to expose a sample to the beam; adetector comprising a number of spectral detection channels sensitive todistinct wavelength sections of the electromagnetic spectrum, whereinthe channels are configured to detect optical signals resulting frominteractions between the beam and the sample and convert the opticalsignals into respective electrical signals; a first optical path fromthe source of the beam to the sample; a second optical path from thesample to the detector; and a signal processing module configured toexecute a lifetime analysis algorithm comprising: receiving theelectrical signals from the detector, wherein the electrical signalsrepresent a time-domain sequence of pulse signals; segmenting thesequence into equal pulse signal segments each comprising substantiallya same number of sampling points, wherein each sampling point of eachpulse signal segment corresponds to a respective sampling index, andwherein a length of each pulse signal segment corresponds to anexcitation pulse repetition period; coherently adding a value of asampling point corresponding to the respective sampling index of eachpulse signal segment to form a supercurve, wherein each sampling indexof the supercurve corresponds to a value equal to a sum of substantiallyall the sampling point values from the corresponding sampling indicesfrom each pulse signal segment, and wherein the supercurve comprises anintensity of at least one lifetime component over time; adding all theintensity values corresponding to each sampling index of the supercurveto generate a sum; normalizing the sum by dividing the sum by a peakvalue of the supercurve; determining a relative intensity contributionof each lifetime component by comparing the normalized sum to a lookuptable; and determining an absolute intensity contribution of eachlifetime component by multiplying the relative intensity contribution bythe peak value of the supercurve.
 12. The apparatus of claim 11, whereinthe signal processing module is further configured to subtract abaseline value from each respective sampling point.
 13. The apparatus ofclaim 11, wherein the signal processing module is further configured tomeasure a baseline shift produced by the at least one lifetime componentas compared to a baseline when the at least one lifetime component isnot present.
 14. The apparatus of claim 13, wherein the signalprocessing module is further configured to subtract the baseline shiftfrom the received electrical signals.
 15. An apparatus for analyzing anoptical signal decay, comprising: a source of a beam of pulsed opticalenergy configured to expose a sample to the beam, wherein the samplecomprises one or more fluorophores having at least one lifetimecomponent; a detector comprising a number of spectral detection channelssensitive to distinct wavelength sections of the electromagneticspectrum, wherein the channels are configured to detect optical signalsresulting from interactions between the beam and the sample and convertthe optical signals into respective electrical signals; a first opticalpath from the source of the beam to the sample; a second optical pathfrom the sample to the detector; and a signal processing moduleconfigured to execute a lifetime analysis algorithm comprising:receiving the electrical signals from the detector, wherein theelectrical signals represent a time-domain sequence of pulse signals;segmenting the sequence into equal pulse signal segments each comprisingsubstantially a same number of sampling points, wherein a length of eachsegment corresponds to an excitation pulse repetition period; measuringa peak baseline shift of the electrical signals based on a presence ofthe at least one lifetime component; and calculating an intensitycontribution of the at least one lifetime component of the sample basedon the measured peak baseline shift and one of: comparison to a lookuptable and numerical fitting.
 16. The apparatus of claim 15, wherein theoptical signals comprise a fluorescence signal.
 17. The apparatus ofclaim 16, wherein the at least one lifetime component is longer than theexcitation pulse repetition period and comprises exogenous fluorescence.18. The apparatus of claim 15, wherein the signal processing module isfurther configured to subtract the baseline shift from the receivedelectrical signals.
 19. The apparatus of claim 18, wherein the signalprocessing module is further configured to: coherently add a value of asampling point corresponding to a respective sampling index of eachpulse signal segment to form a supercurve, wherein each sampling indexof the supercurve corresponds to a value equal to a sum of substantiallyall the sampling point values from the corresponding sampling indicesfrom each pulse signal segment, and wherein the supercurve comprises anintensity of the at least one lifetime component over time; andquantifying an intensity contribution of the at least one lifetimecomponent of the supercurve.
 20. The apparatus of claim 15, wherein thesample comprises a suspension of particles, such that the apparatusfurther comprises: a flow path for the suspension of particles; and aflowcell configured as an optical excitation chamber for generating theoptical signals from interactions between the beam of pulsed opticalenergy and the particles, wherein the flowcell is connected with theflow path, the first optical path, and the second optical path.